Artificial intelligence assisted service provisioning and modification for delivering message-based services

ABSTRACT

Systems and methods involving AI-assisted service provisioning and modification for delivering message-based services. One exemplary method includes: receiving an input sequence from a user in relation to a request for a service, the input sequence including one or more inputs; processing the input sequence to determine a service type; associating a workflow with the request based at least in part on the service type and a profile of the user, the workflow including a set of one or more steps, a step of the set of one or more steps corresponding to a set of attributes including at least one of: a communication mode, a communication type, or a communication priority, the workflow being performed by at least one of: a chatbot, an AI assistant, or a service professional; modifying the workflow based on a new input sequent from the user using a workflow engine comprising at least one of: an AI model, a machine learning model, and a ruleset; and interacting with the user based at least in part on the workflow to deliver the service.

CROSS REFERENCE TO OTHER APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/672,305, filed Nov. 1, 2019 and entitled “ARTIFICIALINTELLIGENCE ASSISTED SERVICE PROVISIONING AND MODIFICATION FORDELIVERING MESSAGE-BASED SERVICES,” now U.S. Pat. No. 10,834,026, issuedNov. 10, 2020, which claims priority to U.S. Provisional PatentApplication No. 62/796,526, filed Jan. 24, 2019 and entitled “ARTIFICIALINTELLIGENCE ASSISTED SERVICE PROVISIONING AND MODIFICATION FORDELIVERING MESSAGE-BASED SERVICES,” the contents of all of which areincorporated by reference in entirety.

COPYRIGHT DISCLAIMER

A portion of the disclosure of this application contains material whichis subject to copyright protection. The copyright owners has noobjection to the facsimile reproduction by anyone of this application,as it appears in the Patent and Trademark Office patent file or records,but otherwise reserve all copyright rights whatsoever.

FIELD OF THE DISCLOSURE

The present invention relates generally to the field of computingtechnology, and more particularly, to artificial intelligence assistedservice provisioning and modification for message-based services.

BACKGROUND

In our modern age, the availability and provision of adequate careservices has not been satisfactory due to a variety of factors. Takinghealthcare for example, medical advancement and innovation continues tofocus on serving the insured population with high to medium income,instead of serving the general public in a more accessible andcost-effective manner.

Even with the deployment of telemedicine powered with low costconnectivity and ample computing resources provided by moderncommunication technologies, the prevalent issues remain to be the lackof sufficient number of doctors, the lack of knowledgeable andpersonalized consultations, as well as the lack of preventive programssuch as awareness education and wellness care that are convenientlyavailable.

Therefore, there exists a need for an effective, efficient, responsive,and self-evolving system for providing services to users in apersonalized and holistic manner.

BRIEF OVERVIEW OF THE DISCLOSURE

The following presents a simplified summary of the disclosure in orderto provide a basic understanding of some embodiments described herein.This summary is not an extensive overview, and it is not intended toidentify key or critical elements to delineate the scope thereof. Someconcepts are presented in a simplified form as a prelude to the moredetailed description that is presented later.

Various embodiments are generally directed to methods of AI-assistedservice provisioning and modification for delivering message-basedservices, executed by a processor coupled to a memory, for receiving aninput sequence from a user in relation to a request for a service;processing the input sequence to determine a service type; associating aworkflow with the request based at least in part on the service type anda profile of the user, the workflow including a set of one or moresteps, a step of the set of one or more steps corresponding to a set ofattributes including at least one of: a communication mode, acommunication type, or a communication priority, the workflow beingperformed by at least one of: a chatbot, an AI assistant, or a serviceprofessional; and interacting with the user based at least in part onthe workflow to deliver the service, comprising: receiving a new inputsequence from the user; analyzing the new input sequence to determine aset of one or more elements, an element of the set of one or moreelements triggering a change in the workflow; in response to thedetermination that the set of one or more elements is not empty,modifying the workflow based at least in part on the set of one or moreelements; and responding to the request based on the workflow.

Various embodiments are further generally directed to systems forAI-assisted service provisioning and modification for deliveringmessage-based services, the systems comprising a memory and a processor.The memory is configured to provide the processor with instructionswhich when executed cause the processor to: receive an input sequencefrom a user in relation to a request for a service; process the inputsequence to determine a service type; associate a workflow with therequest based at least in part on the service type and a profile of theuser, the workflow including a set of one or more steps, a step of theset of one or more steps corresponding to a set of attributes includingat least one of: a communication mode, a communication type, or acommunication priority, the workflow being performed by at least one of:a chatbot, an AI assistant, or a service professional; and interact withthe user based at least in part on the workflow to deliver the service,comprising to: receive a new input sequence from the user; analyze thenew input sequence to determine a set of one or more elements, anelement of the set of one or more elements triggering a change in theworkflow; in response to the determination that the set one or moreelements is not empty, modify the workflow based at least in part on theset of one or more elements; and respond to the request based on theworkflow.

Various embodiments are also generally directed to computer programproducts comprising instructions for: receiving an input sequence from auser in relation to a request for a service; processing the inputsequence to determine a service type; associating a workflow with therequest based at least in part on the service type and a profile of theuser, the workflow including a set of one or more steps, a step of theset of one or more steps corresponding to a set of attributes includingat least one of: a communication mode, a communication type, or acommunication priority, the workflow being performed by at least one of:a chatbot, an AI assistant, or a service professional; and interactingwith the user based at least in part on the workflow to deliver theservice, comprising: receiving a new input sequence from the user;analyzing the new input sequence to determine a set of one moreelements, an element of the set of one or more elements triggering achange in the workflow; in response to the determination that the set ofone or more elements is not empty, modifying the workflow based at leastin part on the set of one or more elements; and responding to therequest based on the workflow.

Various embodiments are disclosed in the following detailed descriptionand the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

The drawings described here are intended to further the understanding ofthe present invention, and form a part of the present application. Theexemplary embodiment in the present application and its description areintended to explain the present invention, and do not constituteinappropriate limitation of the scope of the present invention. Amongthe drawings:

FIG. 1A is a simplified schematic diagram of an example system for AIassisted service provisioning and modification for deliveringmessage-based services, in accordance with one or more embodiments ofthe present disclosure;

FIG. 1B is a block diagram of an example network computing environmentfor AI assisted service provisioning and modification for deliveringmessage-based services, in accordance with one or more embodiments ofthe present disclosure;

FIG. 1C is a block diagram of an example cloud computing environment forAI assisted service provisioning and modification for deliveringmessage-based services, in accordance with one or more embodiments ofthe present disclosure;

FIG. 2A is a simplified block diagram of some of the architecture of asystem for AI assisted service provisioning and modification fordelivering message-based services, in accordance with one or moreembodiments of the present disclosure;

FIGS. 2B-1 and 2B-2 are another simplified example block diagrams ofsome of the architecture of a system for AI assisted serviceprovisioning and modification for delivering message-based service inaccordance with one or more embodiments of the present disclosure;

FIGS. 3A-C are flow charts of example processes for AI assisted serviceprovisioning and modification for delivering message-based services, inaccordance with one or more embodiments of the present disclosure;

FIGS. 4A-G are graphic representations of example user interfaces of aportion of internal UI 214 of FIG. 2A, in accordance with one or moreembodiments of the present disclosure;

FIG. 5 is a simplified block diagram of an example context guard used inconnection with AI assisted service provisioning and modification fordelivering message-based services, in accordance with one or moreembodiments of the present disclosure;

FIGS. 6A-B are graphic representations of example user interfacesshowing example conversations between a user and an example system forAI assisted service provisioning and modification in connection withdelivering message-based services, in accordance with one or moreembodiments of the present disclosure;

FIGS. 7A-B are a simplified flow diagram of an example workflow for AIassisted service provisioning and modification in connection withdelivering message-based services, in accordance with one or moreembodiments of the present disclosure; and

FIG. 8 is a simplified functional diagram of an embodiment of aprogrammed computer system for AI assisted service provisioning andmodification for delivering message-based services, in accordance withone or more embodiments of the present disclosure.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

In order to allow persons skilled in the art to better understand thepresent invention, the technical solution of the present invention isdescribed clearly and completely below in light of the drawingscontained in the embodiments of the present invention. Obviously, theembodiments described are only a portion of the embodiments of thepresent invention, and do not represent the entirety of possibleembodiments. Based on the embodiments contained in the presentapplication, all other embodiments obtained from persons with ordinarytechnical skill in the art without the exertion of creative labor shallfall within the scope of protections of the present application.

It must be noted that the terms “first,” “second,” etc., in thedescription and claims of the present invention, as well as in thedrawings, are used to differentiate similar objects, and are notnecessarily intended to describe a specific order or sequence. It shouldbe understood that data used in this way are interchangeable in theappropriate situations, so that the embodiments of the present inventiondescribed here can be implemented in sequences additional to those shownor described here. Moreover, the terms “comprises” and “has” and anyvariations thereof are intended to cover non-exclusive inclusion; forexample, processes, methods, systems, products, and equipment thatinclude a series of steps or units are not necessarily limited to thesteps or units that are explicitly enumerated, but may comprise othersteps or units that are not explicitly enumerated or are inherent tothese processes, methods, systems, products or equipment.

Various techniques for providing AI-assisted service provisioning andmodification for message-based service delivery are described herein. Invarious embodiments, a service platform receives a request for a servicein user input, and processes the user input to provide the requestedservice to the user by identifying and associating a workflow with therequest. Artificial intelligence models are used to provide assistanceto updating a profile of the user, determining from the user inputelements that trigger changes in the workflow, as well as modifyingruleset(s) used to generate the workflow. In some implementations, theuser input includes a token that indicates a service type of therequested service, the token being pre-configured at the system. In someimplementations, the token is customized according to a service need ofthe user. In some embodiments, the afore-described artificialintelligence models are created, trained, updated, and retrained usingthe communication between the user and/or other users, in relation tothe delivering services of the same service type, and/or deliveringservices other than the service type, at the service platform.

Techniques described herein may give rise to various technical effectsand advantages. For example, the more responsive, informative, helpfuland efficient a message-based service system is providing services toits users, the more likely a user may be to communicate with the systemin the future. The more a user communicates with a system, the more thesystem may be able to learn about the user and the knowledge in servicedomains. Consequently, the system is able to service its users in a moreaccurate, timely, personalized, and holistic manner, increasing thesystem's overall efficiency and conserving computing resources such asmemory, power, processor cycles, and/or network bandwidth.

Furthermore, by segmenting interactions between a user and the systeminto a workflow of a plurality of steps associated with respectiveattributes, the system enables streamlining and automating steps commonto providing services at a finer granularity. For example, a chatbotequipped with the correspondent knowledge (e.g., scripts to conductdomain-specific surveys or questionnaires, scripts personalized based onthe service history) can always be provisioned as a first responder toincoming requests from users to minimize the waiting time from a user'sperspective. At the same time, an automated step can nevertheless betaken over by a human professional, other chatbots or AI assistants,depending on, for example, changes or information newly detected orreceived at the system. Such design further facilitates a smoother, moreeffective, more efficient, and more agile collaboration betweenautomation and professionals, automation and automation, as well asprofessionals and professionals. Empowered this way, the system allows,in a large scale, not only flexible service provisioning as well asmodification, but also effective and efficient learning and training ofautomation.

Referring now to FIG. 1A, a schematic diagram of an example system forAI-assisted service provisioning and modification for deliveringmessage-based services is illustrated in accordance with an embodimentof the present disclosure. System 100 depicts a platform 106 (e.g.,Jiseki platform) in communication with a user (not shown) via a userdevice 102 as well as an affiliate entity (e.g., an employer or partner)103. In some embodiments, platform 106 is configured to interface one ormore chatbots 109 a, one or more AI assistants 109 b, as well as one ormore professionals such as a care professional 109 c (service providersother than care professional 109 c not shown) for the purpose ofdelivering one or more services. In some embodiments, the one or moreservices provided by platform 106 includes, for example and withoutlimitations, clinical care service, mental health services, pet careservice, legal services, dietary care services, dental care services,financial services, support group services, group communicationservices, and therapy services.

In some embodiments, platform 106 includes a plurality of Jiseki tokenconnects 105 a (e.g., channels), through which the user at user device102 (e.g., a desktop device, a mobile device, etc., and the details ofwhich are described below with references to FIGS. 1B-C) can beconnected via a communication network (e.g., wired network, wirelessnetwork, etc., network not shown and more details are described belowwith references to FIGS. 1B-C) by a Jiseki switchboard 105 b intocommunication with one or more of the afore-described one or moreservices (e.g., clinical care, mental health care, financial, etc.).

In some embodiments, switchboard 105 b identifies the user sending theincoming communication (e.g., messages, text, audio, video, data) usingan identifier (e.g., a phone number, voice information, biometricalinformation, messaging app account, email account, social media account,IP address, or any combination thereof), and then accesses the profileof the user stored in system 100 (e.g., the services the user associatedwith the phone number is authorized to access, the context of anypast/current/scheduled conversation(s) the user associated with thephone number is engaged in with platform 106, etc.). In someembodiments, switchboard 105 b further accesses the profile of anemployer/organization the user is associated with. In some embodiments,employer/organization profile information is included in the userprofile information. Based on the profile information, it is determinedwhether to pass through the incoming communication to and therebyconnecting the user to platform 106. Profile information will bedescribed with more details with reference to FIGS. 2A-B.

In some embodiments, different tokens (e.g., keywords, hashtags such as#doc, #money, #talk, @talk, or equivalents in other languages (e.g.,Spanish equivalents such as #medico, #dinero, and #hablar)) are set upat platform 106 to represent each respective service available atplatform 106 (e.g., clinical care, mental health, etc. as shown in FIG.1A). A token can be configured in a variety of forms. For example, atoken can be designed as a word or an unspaced phrase prefixed with adesignated identifier to form a label. A designated identifier includes,for example and without limitations, special characters or symbols suchas the hash character (or pound sign), #; the “at” sign, @; thepercentage sign, %; the dollar sign, $; the acclamation sign, !; thequestion mark, ?; the ampersand sign, &; the asterisk sign, *; and thelike, as well as any combination thereof. A designated identifier canalso include a word, a phrase, string of letters, string of words and/orphrases, string of any combination of letters, words/phrases, andspecial characters/symbols, or any combination thereof, as long assystem 100 is configured with the knowledge that such identifier ismeant as the prefix of a token. In some embodiments, a token can bedesigned as a word or an unspaced phrase post-fixed with theabove-descried designated identifiers.

In some embodiments where incoming communication is not text based,e.g., incoming communication is an audio call, voice messaging, videomessaging, emoji messaging, AR messaging, etc., a token is designed inappropriate forms to suit the nature of underlying communication mode.For example, with voice input, incoming communication is converted intotext using various speech-to-text services (e.g., Google Cloud Speech)to transcribe the speech input into corresponding texts.

In an example where tokens are designed as hashtags, in someembodiments, one hashtag maps to one correspondent service available atplatform 106. For example, #help designates a request for a service thatgives the user a guide of the functions and services at platform 106;#doc designates a request for a medical or clinical service; #dentistdesignates a request for a dental service; #money designate a requestfor a financial service; #tax designates a request for a tax issuerelated service; #card designates a request for a credit card and/ordebt card related service; #sara designates a request for a mentalhealth chatbot service; #SOS designates a request to broadcast anemergency or urgent message to one or more communication groupspre-configured for the users at platform 106; #pet designates a requestfor a pet care related service; #dietitian designates a request for adietary issue related service; and so on.

In some embodiments, one or more hashtags map to one correspondentservice available at platform 106. For example, #doc and #consult canboth designate a request for a clinical or medical service at platform106. In some embodiments, hashtags are customized according to the needsand requests of users associated with a particular organization or userssubscribing to a particular service. For example, for employeesassociated with a company A only, #allergy is recognized and mapped to arequest for clinical care related to pollen-caused allergies accordingto the service provisioned for company A (located in a neighborhoodhaving abnormal large population of flowering plants) at platform 106.However, when a user other than an employee of company A communicates#allergy to platform 106, the user is either not allowed access to theafore-described service associated with #allergy, or is only allowed toaccess a general allergy care, without the default assumption of thecause of the allergy. In some embodiments, one single hashtag maps todifferent levels of services available at platform 106. For example,depending on the level of services a user has signed up, or the user'sorganization has signed up on behalf of the user, #sara is mapped to amental health service chatbot equipped with access to differentknowledge bases or data stores corresponding to the respectivesubscription level. In some embodiments, hashtags are deleted when theusage of them become obsolete. In some embodiments, specially designedhashtags are time sensitive and only in effect through a designatedperiod of time to address the needs for specific services during aperiod of time.

If the incoming communication (e.g., an input sequence) includes arecognized token, such as a hashtag, it is compared against the user'saccess permissions (e.g., whether the user can access the serviceactivated by the token) as a further filter. Once the user's message issent through, the interaction may be automated, manual, or a combinationthereof, as determined by the pre-determinedprotocols/algorithms/rulesets/AI and the workflows the user is in themidst of. In some embodiments, each service channel has its ownworkflows and messages that flow back to the user, sometimes unprompted.

In some embodiments, authorized users at user device 102 use thosetokens, which involves/entails sending platform 106 messages thatcontain the tokens, to connected to corresponding services. Certainselected services may only be accessible to certain users based oninformation such as organizational affinity (e.g., employees of acertain company purchasing a portion of service access to platform 106can only talk to Jiseki's financial advisor, or a patient referred by amental health professional can only interact with service availablethrough #talk), profile, or payment/membership status, etc. Unknownusers may be able to use certain tokens to access specific services atno cost, and many other users can be pre-imported into the system to berecognized when they message platform 106.

In some embodiments, platform 106 includes one or more smart bots 109 a,one or more AI and/or machine learning enabled assistants 109 b inaddition to interfacing one or more professionals such as a careprofessional 109 c. The smart bots 109 a, AI assistants 109 b, and theprofessionals (e.g., service providers such as a nurse, doctor, lawyer,CPA, therapist, physiatrist, social worker, etc.) participate inproviding the afore-mentioned one or more services to the user. In someembodiments, the afore-described professionals include their own smartbots (not shown), AI assistants (not shown), or any form of machinelearning entities (not shown). In some embodiments, smart bot 109 a(e.g., chatbot) is implemented using a combination of one or more rules,and/or keyword analysis. For example, a chatbot can be designed to mimichuman conversation. In some embodiments, AI assistant 109 b isimplemented using artificial intelligence and/or any form of machinelearning, and/or AI algorithms. In some embodiments, smart bot 109 a istasked to provide chat services to the user in a style of scripted orrule-based conversation (e.g., structured conversation). In someembodiments, AI assistant 109 b is tasked to provide chat services tothe user in a style of open ended, free flowing conversation (e.g.,unstructured conversation). In some embodiments, structured conversationtypes refer to defined interactions (e.g., surveys) or a particularpre-crafted message defined in the workflow rulesets. In someembodiments, an unstructured conversation, or freeform conversation,results from either platform 106 or the user deciding to engage in anundefined (e.g., discovery) process. This could mean conversation with aprofessional, but it may also mean conversation with AI assistant 109 bcapable of natural language processing. Based on the results of eitherone or both, it is determined whether a new workflow is to be generated,or an existing workflow is to be customized. As described with moredetails below with references to FIGS. 4A-G, in some embodiments, anoperational dashboard (e.g., a professional console UI, internal UI,control UI) is configured to enable the professional to perform eitheror both of these afore-described tasks (e.g., generating a new workflow,modifying an existing workflow) manually. In some embodiments, suchtasks are performed automatically after and/or assisted by analyzing theconversation pattern, and/or information collected during the servicedelivery. In some embodiments, a workflow is initiated according to astructured conversation and performed by smart bot 109 a (e.g., arule-based bot, chatbot). In some embodiments, chatbots and AIassistants both refer to any form of conversation automations.

In some embodiments, platform 106 is configured to include variousadministrative components 107, which are used, among other things, toprovide platform 106 with various functionalities and/or data requiredin order to undertake the delivering of the one or more services to theuser. For example and as illustrated herein, administrative components107 include Jiseki engineering 107 a and Jiseki corporate 175 b. Jisekiengineering 107 a is configured to enable entities such as Jisekiadministration and/or engineering personnel to undertake variousfunctions pertaining to, for example and without limitations, the setupof platform 106, provisioning and configuration of platform 106, andmaintenance and modification of platform 106. In some embodiments,Jiseki engineering 107 a enables Jiseki administrative/engineeringpersonnel to implement rulesets that dictate, for example and withoutlimitations, how to associate a user with a service (e.g., a workflow ofa service) based on various conditions, how to modify a workflow basedon user input information, the profile data, chatting history data onthe particular channel associated with the service and/or the user,chatting history data on channels other than the particular channelassociated with the service and/or the user, and so on. Furthermore,Jiseki engineering 107 a enables Jiseki administrative/engineeringpersonnel to implement tokens and their mapping relationship withcorresponding services, set up individuals and/or organizations whosemembers/affiliates/agents for access/subscription to groups ofparticular one or more services provided at platform 106, and registerprofessionals (e.g., agent) with authorized role-based access toplatform 106 to service users.

In some embodiments, Jiseki engineering 107 a enables the access tothird party tools (e.g., payment services, calendar appointment service,medication prescription service, etc.), in connection with deliveringthe afore-described one or more services to the user. More details aredescribed below with reference to FIGS. 2A-B.

In some embodiments, Jiseki corporate 107 b is configured to create,manage, and maintain, for example and without limitations, userprofiles, organization/employer profiles, subscription rules, accessrules, privacy policies, one or more databases mapping tokensdesignating various channels corresponding to various services, etc. Insome embodiments, Jiseki corporate 107 b interfaces with one or moreaffiliate entities such as employer/partner 103 to provide and/orcustomize services to the employee/agents associated withemployer/partner 103. In some embodiments, Jiseki corporate 107 b alsointerfaces with individual users to provide/customize services deliveredthrough platform 106.

In some embodiments, Jiseki cooperate 107 b engages directly withusers/parties for delivering services through, for example and withoutlimitation, advertisement and/or direct recruitment. On the other hand,Jiseki corporate 107 b also engages employer/partner 103 (e.g., thirdparty employers, organizations, government partners, etc.) to sign upusers with services (e.g., pre-authorized services, pre-paid services,etc.) in association with service channel designating tokens (e.g.,generic Jiseki token, customized token, time-sensitive token, etc.).Although only one user device 102, one Jiseki platform 106, one smartbot 109 a, one AI assistant 109 b, one care professional 109 c, oneJiseki engineering 107 a, one Jiseki corporate 107 b, oneemployer/partner 103, and a limited number of services and theircorrespondent channels are shown in FIG. 1A, system 100 can include anynumber of user devices 102, Jiseki platforms 106, employer/partner 103,as well as services and professionals; and Jiseki platform 106 caninclude any number of smart bots 109 a, AI assistants 109 b, careprofessionals 109 c, as well as services in other embodiments.

FIG. 1B illustrates an example network computing environment for AIassisted service provisioning and modification for deliveringmessage-based services in accordance with an embodiment of the presentdisclosure. The network computing environment includes one or moreclients 102 a-102 n (also generally referred to as client(s) 102, clientdevice(s) 102, user device(s) 102, and end device(s) 102) incommunication with one or more servers 106 a-106 n (also generallyreferred to as server(s) 106) via one or more networks 104.

In some embodiments, clients 102 and servers 106 are configured on thesame network 104. In some other embodiments, clients 102 and servers 106are in communication via multiple networks 104 configured therebetween.In some embodiments, network 104 is a private network, a public network,or a combination thereof. In some embodiments, network 104 includes oneor more networks such as a data network, a wireless network, a telephonynetwork, or any combination thereof. It is further understood that thedata network can be any body area network (BAN), personal area network(PAN), local area network (LAN), metropolitan area network (MAN), widearea network (WAN), public data network (e.g., the Internet), and thelike. The wireless network can be, for example, a cellular networkemploying various technologies such as general packet radio service(GPRS), global system for mobile communications (GSM), Enhanced Data GSMEnvironment (EDGE), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), or the like; as wellas any other suitable wireless medium, e.g., worldwide interoperabilityfor microwave access (WiMAX), code division multiple access (CDMA),wideband code division multiple access (WCDMA), wireless fidelity(Wi-Fi), wireless LAN (WLAN), Bluetooth®, Bluetooth Low Energy (BLE),ANT/ANT+, Z-Wave, ZigBee, NFC, infrared channel, and the like, or anycombination thereof. Network 104 can be an overlay network that is avirtual network enabled on top of one or more layers of other network104. Network 104 can also be any such network, known to those ordinarilyskilled in the art, that are capable of supporting the operationsdescribed herein. Network 104 can be implemented using varioustechniques and layers or protocol stacks including, for example andwithout limitations, Ethernet protocol, the internet protocol suit(TCP/IP), the ATM (Asynchronous Transfer Mode) suit, the SONET(Synchronous Optical Networking) protocol, the SDH (Synchronous DigitalHierarchy) protocol, and the like.

In some embodiments, server 106 may include a group of servers 106(e.g., multiple logically-grouped servers 106, server farm(s) 106). Insome embodiments, servers 106 may be geographically dispersed. Servers106 within a server farm can include both heterogeneous servers andhomogenous servers. In other words, one or more servers 106 within aserver farm can operate according to one type of operating system (e.g.,Windows), while one or more of the other servers 106 can operateaccording to other types of operating systems (e.g., Linux, Unix, MacOS).

In some embodiments, servers 106 in a server farm may be implemented athigh-density rack systems with associated storage system, and located ina consolidated space (e.g., a data center, an enterprise center). Inthis way, various techniques can be implemented to improve systemperformance, data security, physical security, and system manageability.In some embodiments, servers 106 within a server farm may not bephysically proximate to another server 106 within the same server farm.The logically-grouped servers 106 as a server farm can be interconnectedusing a wide-area network (WAN) connection, a metro-area network (MAN)connection, or the like. For example, a server farm can include servers106 physically located in different continents, regions of thecontinents, countries, states, cities, data centers, etc. Thelogically-grouped server 106 as a server farm can also be interconnectedusing local-area network (LAN) connection or other forms of directconnection with increase speeds of data transmission.

Server 106 may be a messaging server, file server, application server,web server, proxy server, appliance, network appliance, gateway, gatewayserver, virtualization server, deployment server, SSL VPN server,firewall, or the like. In some embodiments, a plurality of servers 106may be in the path between any two communicating servers 106.

Client devices 102 can be any type of computing device (e.g., a devicewith a processor and memory) such as a mobile terminal, fixed terminal,or portable terminal. For example, client devices 106 can include amobile phone, a smart phone, a smart watch, a tablet, a desktopcomputer, a laptop computer, a Personal Digital Assistants (PDAs), amedia playing device, a gaming system, an in-vehicle device, an IoTdevice, a wearable device, a server, and the like. Client devices 106are equipped with sufficient processing power and memory capacity toperform the operations described herein. In some embodiments, clientdevice 102 can include different operating systems, processors andinput/output components configured for the device. In some embodiments,client device 102 may include a combination of devices, e.g., a smartphone combined with a wearable smart fitness device. In someembodiments, client devices 102 may function as both a node incommunication with a server and as a server providing access to hostedresources for other clients 102.

FIG. 1C illustrates an example cloud computing environment for AIassisted service provisioning and modification for deliveringmessage-based services in accordance with an embodiment of the presentdisclosure. A could computing environment is configured to provideclients 102 with resources provided by the afore-described networkcomputing environment. The cloud computing environment includes one ormore clients 102 a-102 n in communication with cloud 108 via one or morenetworks 104. Client 102 may include, for example and withoutlimitations, a zero client, thin client, as well as zero client. As usedherein, both a thin client and a zero client refer to a client that maydepend on cloud 108 and/or server 106 in order to provide some of itsfunctionality. A zero client may further depend on cloud 108 and/orserver 106 in order to retrieve operating system data for client 102. Athick client may be configured to provide some functionality whendisconnected from cloud 108 or server 106. Cloud 108 may be public,private, or a combination thereof (e.g., hybrid). Public cloud may beconnected to servers 106 over one or more public networks; while privatecloud may be connected to private servers 106 via one or more privatenetworks. In some embodiments, private cloud can include private serversthat are maintained by clients 102 or owners of clients 102. Hybridcloud may include both private and public servers, as well as bothprivate and public networks.

Cloud 108 can include a cloud based services, for example and withoutlimitations, Software as a Service (SaaS) 110, Platform as a Service(PaaS) 112, and Infrastructure as a Service (IaaS) 114. As used herein,IaaS refers to online services that deliver various underlying computingfunctionality such as storage, networking, server, security,virtualization, backup, and the like. In some embodiments, IaaS allows auser to access (e.g., rent) the afore-described computing infrastructurethat are needed during a specific period of time period in order toscale up quickly. Examples of IaaS includes Google Compute Engineprovided by Google Inc., Rackspace Cloud provided by Rackspace Inc., andAmazon Web Services (AWS) provided by Amazon Inc. PaaS offers, inaddition to those computing functionalities provided by IaaS, computingresources including, for example and without limitations, the operatingsystem, middleware, database, or runtime resources. Examples of PaaSinclude Windows Azure provided by Microsoft Corp., Google App Engineprovided by Google, Inc., and Oracle Cloud provided by Oracle Corp. SaaSproviders offer additional resources such as application and dataresources. Examples of SaaS include Google Apps provided by Google Inc.,Office365 provided by Microsoft Corp., Dropbox provided by Dropbox Inc.,iCloud provided by Apple Inc.

Client 102 may access IaaS services using one or more IaaS standardsincluding, for example and without limitations, Open Cloud ComputingInterface (OCCI), Cloud Infrastructure Management Interface (CIMI),Amazon Elastic Compute Cloud (EC2), and OpenStack standards. Client 102may access PaaS services using one or more PaaS interfaces including,for example and without limitations, HTTP, Java APIs, JavaMail API, JavaData Objects (JDO), Java Persistence API (JPA), Python APIs, webintegration APIs such as Rack for Ruby, WSGI for Python, and PSGI forPerl, and the like. Client 102 may access SaaS resources throughweb-based user interfaces provided by, for example and withoutlimitations, a web browser (e.g., Google Chrome, Mozilla Firefox,Microsoft Internet Explorer). Client 102 may also access SaaS resourcesvia applications on smartphones, tablet or smart devices; as well asthrough the operating system of client 102.

FIG. 2A illustrates an example block diagram of some of the architectureof a system for AI assisted service provisioning and modification fordelivering message-based service in accordance with an embodiment of thepresent disclosure. System 200 includes a client 202 in communicationwith a platform 206 via a network 204. At client 202, a user (not shown)sends an input sequence over network 204 to platform 206 in order torequest a service and/or receive and engage in communication withplatform 206. In some embodiments, client 202 can be implemented by, forexample and without limitations, client 102 of FIGS. 1A-C; and network204 can be implemented by, for example and without limitations, network104 of FIGS. 1A-C. Platform 206 can be a part of one or more servers106. In some embodiments, tasks performed by platform 206 can beperformed by a plurality of servers 106, e.g., being allocated amongstthe plurality of servers by an application, service, process, daemon,routine, executable logic, or other task allocation techniques. In someembodiments, platform 206 can be implemented at, for example and withoutlimitations, servers 106 of FIGS. 1A-B, and/or cloud 108 of FIG. 1C.

In some embodiments, platform 206 includes a message execution module210, a conversation management module 212, a rule engine 218, anInternal UI module 214, and a set of one or more third party services216. In some embodiments, it should be appreciated that the set of oneor more third party services 216 can also operate independently fromplatform 206.

Each of platform 206, message execution module 210, conversationmanagement module 212, internal UI module 214, rule engine 218, set ofone or more third party services 216, and any components or sub-modulesthereof, can be embodied as hardware, software, firmware, or acombination thereof. Such software can include, for example and withoutlimitations, a program, service, task, script, library, application,service, or any form of executable instructions on one or moreprocessors. Any of these afore-described modules, components, andsub-modules may be combined into one or more modules, components,sub-modules, applications, programs, services, tasks, scripts,libraries, or executable code. For example, the various modules,components, sub-modules, and logic of platform 206 can form a portionof, or otherwise be established by, the processor 802 of FIG. 8 or othercomputing device.

In some embodiments, message execution module 210 includes a listener221, a communication module 222, an input cache 223, a router 224, and aresponse queue 225. In some embodiments, along an inbound path, listener221 is configured to receive inputs incoming from the user to platform206, and in turn forward the received inputs from the user to inputcache 223 for forwarding to router 224, which eventually furtherforwards the received inputs to conversation management module 212 forprocessing. Along an outbound path, router 224 is configured to receiveone or more responses in relation to the inputs from conservationmanagement module 212, and store the received one or more responses inresponse queue 225, which in turn forwards the one or more responses tocommunicate module 222 including worker such as text worker 222-a, voiceworker 222-b, video worker 222-c, email worker 222-d, and web worker222-n, etc. Communication module 222 is configured to eventuallytransmit the one or more responses to the user as a result of receivingthe inputs from the user according to the appropriate communication modeenabled by its workers.

In some embodiments, listener 221 can be configured as a portal inreceipt of SMS or MMS based incoming communication addressed to a shortcode. In some embodiments, a short code is a sequence of five or sixdigits of phone number, or a sequence of any digits of phone number,associated with platform 206. In some embodiments, listener 221 can beconfigured with one or more short codes that are associated withplatform 206. For another example, listener 221 can be configured as aportal in receipt of messages addressed to one or more accounts of amessaging application (e.g., Facebook Messenger®, SnapChat®, Slack®,iMessage®, KaoKaoTalk®, DingTalk®, Skype®, WhatsApp®, Line®, WeChat®,Telegram®, Discord®, Instagram®, Google Hangouts®, Twitter®, avatarmessaging, virtual reality application, gaming application, and thelike) that are associated with platform 206. In some other examples,listener 211 can be configured to receive inputs from the user informats other than text such as email, voice, video, etc.

In some embodiments, router 224 is configured to process the inputsequence and determine whether the received input sequence includes anytokens (e.g., hashtags) recognizable at platform 206, and accessible tothe particular requesting user. In some embodiments, router 224 isfurther configured to retrieve the profile information associated withthe requesting user from a profile store 238 to identity the detailedservice level provisioned for the particular user corresponding to thereceived token(s). This way, a service type is determined from theincoming input sequence. In some embodiments, router 224 is configuredto also forward a copy of the input sequence to internal UI 214 fordisplaying. For example, incoming input sequence can be displayed via aconversion dashboard 241 of internal UI 214.

In some embodiments and as illustrated herein, communication module 222includes one or more of a text worker 222-a, a voice worker 222-b, avideo worker 222-c, an email worker 222-d, and a web worker 222-n. Viatext worker 222-a, platform 206 is configured to generate andcommunicate text messages with the user in any appropriate manner. Viavoice worker 222-b, platform 206 is configured to generate andcommunicate voice messages with the user in any appropriate manner. Viavideo worker 222-c, platform 206 is configured to generate andcommunicate video messages and/or sessions with the user in anyappropriate manner. Via email worker 222-d, platform 206 is configuredto generate and communicate email messages with the user in anyappropriate manner. Via web channel 222-n, platform 206 is configured togenerate and communicate landing web pages and/or sessions with the userin any appropriate manner.

In some embodiments, communication modes to be executed by communicationmodule 222 are determined based on the response(s) fetched from responsequeue 225 for transmission to the user. In some embodiments, responsesgenerated from conversation management module 212 are stored in responsequeue 225 and configured with an indicator that designates in whichmanner the responses are to be communicated to the user. In someembodiments, the indicator is determined based on the communication modeinformation specified by the steps of a workflow that is identified andassociated with the users in response to the inputs therefrom and/or theprocessed inputs. For example, when the user's input to platform 206triggers a response from a care professional (e.g., a doctor) that hasto be communicated in compliance with patient privacy standards setforth in regulations such as, for example and without limitations, theHealth Insurance Portability and Accountability Act (HIPAA), a HIPAAcompliant video link is included in a text message for transmission tothe user as part of the response. Upon clicking on the video linkembedded in the message, the user traverses to a HIPAA-enabled landingweb page in a web browser to consult with the care professional, withthe ease of mind that none of the user's privacy information such asmedical records or data are exchanged or accessed beyond the bounds ofgoverning laws, regulations, and/or guidelines. Using the same contextof the user consulting with a care professional on medical matters, at acertain point of the HIPAA-enabled communication, the care professionalmight decide to ask a nurse to send the user some self-educationalmedical materials so that the user can become more knowledgeable ofcertain medical matters. As text messages are usually limited in thenumber of words, an email link can be embedded in a text message to betransmitted to the user, and an email can also be generated and sent tothe user at an email account known to platform 206.

In some embodiments, platform 206 is configured to generate a set of oneor more responses in receipt of an input from the user. In someembodiments, each response of the set of one or more responses bears thesame type of communication mode indicator such that each response is tobe transmitted to the user via the same communication mode bycommunication module 222. For example, all the responses of a set of oneor more responses can be sent to the user via SMS. In some otherembodiments, one or more responses of the set of one or more responsescan have a communication mode indicator different than the other one ormore responses of the set of one or more responses. For example, inreply to an input from the user, a first response of a set of responsesis to be transmitted via SMS, a second response of the set of responsesis to be transmitted via an audio link, and a second response of the setof response is to be transmitted via SMS again. In some embodiments, themultiple communication modes associated with a set of one or moreresponses are determined based on the communication modes of the one ormore steps associated with a workflow identified and assigned toaddressing the inputs of the user. In some embodiments, thecommunication mode associated with a particular step is a fixed mode. Insome other embodiments, the communication mode associated with aparticular step can be modified based on the information/contentexchanged between platform 206 and the user, platform 206's knowledge orany types of intelligence of the user, or groups of users.

In some embodiments, text worker 222-a is configured to build a textmessage after receiving a response from response queue 225. For example,text worker 222-a can be configured to fetch a text template from astore (e.g., response store 237) based on the information in theresponse to build the text content of the text message. Text worker222-a can also be configured to include the text from the response inthe text content of the text message. Text worker 222-a can further beconfigured to include the information of the user's mobile phone number,the user specific messaging application and account information, fromthe user's profile stored at profile store 239 to fully compose the textmessage, and send it through, for example, Twilio service.

In some embodiments, email worker 222-d is provided by running aninstance of an application that generate the desired message types, suchas running a Gmail® application, Microsoft Outlook®, any other type ofemail application; or by accessing a cloud-based email service (e.g.,Amazon Simple Queue Service). The email message may be generated to beappropriately formatted for specific messaging platforms, for exampleand without limitations, Gmail client, Outlook 365, Outlook Web Access(OWA), Webmail, iOS, and the like. In some embodiments, email worker222-d is configured to build an email message after receiving a responsefrom response queue 225. In some embodiments, email worker 222-d isconfigured to use the information in the response to fetch a templatefrom a data store as the detail page of the email message, and use otheruser specific information to populate the detail page, put headers(e.g., sender's email address, user's email address, subject line,priority level, etc.) to the email to fully compose the email message.Afterwards, email worker 222-d is configured to utilized one or moreemail servers to send the email to the user. In some embodiments, emailworker 222-d is configured to include information that is entered by aservice professional or otherwise generated by platform 206 for beingincluded in the response into the detail page.

In some embodiments, website worker 222-n Is configured to furnish alanding page upon, for example, the user clicking on a link included ina text message or an email message, or otherwise interacts with thecontent sent to them. In some embodiments, website worker 222-n isconfigured to retrieved the landing page from a template page store (notshown) In some embodiments, website worker 222-n is configured to trackinformation such as what browser the user is using when interacting witha link, which web content the user interacts once traversing to thelanding page, and so on.

Conversation management module 212 includes a classify and summarizesub-module 231, a conversation tracker 232, a workflow manager 233, aruleset store 234, a real-time processing sub-module 235, a scoring andclassification sub-module 236, a response store 237, and profile store238.

In some embodiments, conversation management module 212 is configured toreceive user input (e.g., input sequence) from message executing module210. In some embodiments, classify and summary sub-module 231 receivesthe user input from router 224 of message execution module 210. Afterthe analyzing (e.g., identifying, classifying, summarizing, scoring,and/or ranking, etc.), the user input, and/or the analysis results areforwarded to workflow manager 233. In some embodiments, classify andsummary sub-module 231 intelligently analyzes use input by applyingautomated AI classification and/or reasoning techniques. For example,classify and summary sub-module 231 can resolve ambiguities, infer acontext to achieve improved understanding of the user's goal, intent,information meant to be provided with respect to the communication. Insome embodiments, classify and summary sub-module 231 applies speechrecognition, natural language processing, and/or AI techniques tointerpret the user input and determine the user's likely intent, goal,or information meant to be provided with regard to the currentcommunication at platform 206. By invoking real-time processingsub-module 235, classify and summary sub-module 231 analyzes the userinputs in a real time manner. By invoking scoring and classificationsub-module 236, classify and summary sub-module 231 classifies the userinputs with a confidence score. Based on its processing of the userinput, classify and summary sub-module 231 looks for one or moreelements that trigger a change in a workflow, such as informationrelevant to the values of the variables specified in the ruleset, whichis determined based the service type determined by router 224,likelihood of seeking a service other than the present one, likelihoodof seeking a specific sub-specialty of the present service, and thelike. In some embodiments, such elements are not present in the userinput and there is no need to modify the workflow. In some embodiments,such elements are determined based on the answers the user provides tosurveys conducted in one or more steps of the workflow. In someembodiments, classify and summary sub-module 231 processes the userinput using the user profile information stored at profile store 238.This way, classify and summary sub-module 231 determines that the userinput may have different meanings depending on the user's contextinformation, such as the user profile information, previouscommunication of the user at platform 206, on-going communicationrelated to other services at platform 206, and the like. In someembodiments, probabilistic or statistical models (e.g., Bayesianmodeling) are used to compute the likelihood, probability, or degree ofconfidence or certainty with which the user input can be associated witha particularly intended meaning based on the user profile. For example,certain user input may have a different meaning depending on whether theuser is travelling (out of town) or at home base (in town), whether theuser has experienced a major life event (a first newborn child), whetherthe user has requested the same type of service before, if so, how longago, and the like. In some embodiments, classify and summary sub-module231 determines that the user input may have different meanings dependingon the information and data of other users. For example, given aparticular timing and geo-location, classify and summary sub-module 231determines that an user input of “coughing” may have a meaning of“coughing because of the air pollution caused by forest fires” based onthe observation of the communication from other users in the samegeo-area and around the same period of time, instead of “coughingbecause of a cold” in other contexts.

After receiving the user input and/or the analysis results, workflowmanager 233 is configured to, for example, associate a workflow with theuser, and in turn communicate with internal UI 214 so that the user'sinputs and respective workflow are displayed and controlled via internalUI 214 (e.g., Workflow Controller 242). Internal UI 214 can indicatethat a workflow has been provisioned with a chatbot which has startedconversing with the user; or the workflow awaits the handling by eitherby an agent (e.g., a professional, or an administrator) of platform 206.More details are described below with reference to FIG. 4A-G.

Conversation tracker 232 is configured to keep a record of all thecommunications exchanged between the user and platform 206. In someembodiments, conversation tracker 232 can store all the non-HIPAArelated communication and data in one data store (e.g., conversationhistory 261 of FIG. 2B-1), and all the HIPAA related communication anddata in another data store (e.g., Jiseki HIPPA data store 255 of FIG.2B-1). In some embodiments, conversation tracker 232 further separatelystores all the responses generated at platform 206 in response store237. In some embodiments, conversation tracker 232 further separatelystores all the inputs generated from the user at, for example, a Jisekianalytics store 267 of FIG. 2B-2. In some embodiments, conversationtracker 232 communicates with third party services 216 so as to recordthe communication between the user and third party services, and thecommunication between platform 206 and third party services 216. In someembodiments, Conversation tracker 232 communicates with internal UI 214(e.g., dashboard 241) so that internal UI 214 can access all thecommunication and data related to the user, platform 206 and third partyservices.

Workflow manager 233 is configured to associate a workflow with the userrequest based on the determined service type and the user profileinformation. In some embodiments, based on the service type and/orprofile information, workflow manager 233 identifies a rulesetcorresponding to the token include in the user input. For example, uponreceiving #doc, a general clinical ruleset is retrieved from rulesetstore 234, and applied to rule engine 218 for evaluation, the output ofwhich produces a corresponding general clinical workflow (using thesteps in response store 237). In this example, the ruleset indicates fora general medical questionnaire to be presented to the user by achatbot, if no preference is specified otherwise. Rule engine 218evaluates the ruleset using the profile information and the user inputinformation. If either one including an element that indicates thepreference as “non bot,” rule engine 218 outputs a workflow ofpresenting the medical questionnaire by an agent. Otherwise, rule engine218 outputs a workflow of presenting the medical questionnaire by achatbot.

In some embodiments, rule engine 218 evaluates a default ruleset usingthe WPC scorings stored in the profile of the user prior to evaluating aruleset corresponding to the identified service type in order togenerate a workflow. For example, a rule in the default ruleset canspecify to always apply the rulesets of the categories having a higherscoring sooner than those having a lower scoring. For another example, arule in the default ruleset can specify to generate a workflow forfollowing up with the user in a particular category, other than therequested service type and with a scoring exceeding a pre-configuredthreshold, in addition to generating the workflow responding to therequested service.

Workflow manager 233 is also configured to modify the workflowassociated with the request based on the user inputs and the userprofile information. In some embodiments, workflow manager 233 firstidentifies a corresponding ruleset addressing the determined servicetype from ruleset store 234. Next, workflow manager 233 applies theobtained ruleset to rule engine 218 so as to obtain a workflow of steps,which are identified from a reservoir of existent steps (e.g., responsestore 237). In some embodiments, the service request is sufficientlyspecific (e.g., #dentist) to be used to retrieve the specificcorresponding ruleset. In some other embodiments, a service request isrelatively general (e.g., #doc, #sore throat), then a corresponding highlevel ruleset is retrieved accordingly. In some embodiments, a highlevel ruleset is implemented in a recursive structure (e.g., a treestructure) so that the execution of the steps in the workflow determinedbased on the high level ruleset (e.g., general medical ruleset) leads toidentifying the next level ruleset, and so on. For example, theexecution of a step of the initially identified workflow leads to thegathering of new user input (e.g., answers to newly identified surveysor questionnaires) and information derived therefrom, which furtherleads to rule engine 218 identifying other rulesets upon evaluating thehigh level rule with the new user input and information. For example,upon executing a step of a workflow associated with a general medicalservice request (#doc), a ruleset addressing medical specialties(#rhinosinusitis) can be identified for subsequent evaluation by ruleengine 218. Furthermore, modified workflows can be stored in responsestore 237.

Once a workflow is associated with the user request, workflow manager233 is configured to execute the steps of the workflow accordingly. Forexample, for an automation step, workflow manager 233 will deploy acorrespondent chatbot or AI assistant according to the communicationmode, type, and priority specified for the step. For a step that needsservice professional's handling, workflow manager 233 queues therespective user request to a list and causes internal UI 214 to updatethe display of the list accordingly so that professionals can claim theuser request off the list via internal UI 214. For a step to wait for anordered lab results, workflow manager 233 causes internal UI 214 toalert the professional once the lab results become available. In someembodiments, workflow manager 233 is further configured to automaticallyflag a user request to better describe/capture the nature of therequest. In some embodiments, internal UI 214 allows professionals toadd and remove flags in association with a user's request. Uponcompletion of all the step in a workflow, workflow manager 233dis-associates the workflow from the user request.

Workflow manager 233 also communicates with workflow tracking 245 ofinternal UI 214 so that professionals at internal UI 214 can access,view, flag, group, and operate on workflows associated with varioususers' requests in suitable manners. Once a workflow is associated witha user request, all the information of the workflow is relayed tointernal UI 214 for display. For example, a mental health careprofessional can access all the chats exchanged between Sara (AIassistant) and the user who is seeking mental health related help atplatform 206 before deciding it is the right time to take over theconversation. Workflow control 242 of internal UI 214 communicates withruleset store 234 so that professionals at internal UI 214 can manuallyengage the user to carry out one or more steps, provision the workflowfor execution by other professionals and/or chatbots, modify theruleset, modify the workflow, release the workflow from association withthe user request, and the like. For the workflows that are manuallymodified by professional via internal UI 214, the underlying pattern canbe captured in terms of updates to the ruleset corresponding to theworkflow, updates to the diagnostic questionnaires related to theruleset, as well as updates to the domain knowledge base at platform206.

A workflow is a collection of steps corresponding to a chain of eventsand processes/actions starting from a first engagement (e.g., an intake)of a user who sends a request for a service, until the requested serviceis fulfilled and the user released from the workflow. Taking medical orclinical services for example, a medical workflow may include a set ofone or more steps (ordered or not, or partially ordered) that encompassa patient intake with pertinent diagnostic processes (e.g., questions,lab orders, modality orders, and the like); diagnosis of a condition;treatment (e.g., prescription of medication treatment, physicaltreatment, and the like) of a condition; follow-ups; etc. The workflowoftentimes entails cooperation of a variety of entities such asautomation entities (e.g., smart bots, chatbots, AI assistants), humanprofessionals or agents (e.g., doctors, clinical staff, nutritionadvisors, physical therapists, mental health care providers,pharmacist), third party services (e.g., payment service, onlineprescription services), as well as involving a large amount of data andinformation.

Steps of a workflow correspond to specific tasks/actions to be performedduring the course of delivering the service based on the workflow. Forexample, diagnosis of a condition can be performed by a step ofactivating a chatbot equipped with the corresponding diagnosticquestionnaire (further including a step of platform 206 providing a linkto a HIPAA compliant platform on which the chatbot is to engage theuser), and/or a step of facilitating the user making an appointment witha selected medical doctor (further including a step of platform 206providing a link to a calendar service, and/or a payment service). Foranother example, treatment can be performed by a step of a chatbotsending the user a care plan (e.g., when simple home remedy seemssufficient based on the diagnosis), a step of platform 206 providing thetreating doctor with an online prescription service to prescribemedication for the user, a step of platform 206 allowing the doctor tosend the user emails, documents (e.g., educational materials), a step ofa chatbot reminding the user to take the medication timely, and thelike.

In some embodiments, steps of workflows are indexed to the respectiveaction or event type. For example, a step of generating a HIPAAcompliant communication link for embedment into a SMS message to theuser is indexed under, e.g., HIPAA link. For another example, onceplatform 206 narrows down the user's request from a relatively generalone (e.g., #doc) to a relatively specific one (e.g., sore throat, flu)after obtaining more information from the user (e.g., by conductingdiagnostic questionnaire guided communication, which is described withmore details below), a step of a chatbot sending the user a care plan ismapped to the chatbot sending a care plan specific to the identifiednarrowed request.

In some embodiments, each step of a workflow corresponds to at least oneof the following attributes: communication modes, communication types,and communication priority. Communication modes include, for example,text, voice/video call, HIPAA compliant versions of text and voice/videocall, and web form, etc. Communication types include, for example,conversation initiated by professionals such as doctors and nurses,bots, or AI assistants. Communication priority includes, for example, alevel of priority associated with the user in terms of how soon platform206 is expected to execute the requested services to the user. Asfurther illustrated with reference to FIGS. 7A-B, steps of a workfloware performed by a combination of chatbots, AI assistants, andprofessionals.

In some embodiments, workflows are determined based on the results ofrule engine 218's evaluating of one or more rules included in a rulesetcorresponding to the service request. In some embodiments, a workflow ismodified by workflow manager 233 based on inputs from the user, and/orthe user profile information. In some embodiments, a workflow isprovisioned and/or modified by workflow manager 233, based oninformation and data observed and derived at platform 206, fromcommunication between platform 206 and other users. Workflow manager 233can add one or more steps to a workflow, delete one or more steps from aworkflow, expand a step into multiple steps, modify the attributesspecified at one or more steps of a workflow, and the like.

In some embodiments, changes captured in the user profile informationtriggers workflow manager 233 to modify the step attributes, and/orre-apply rulesets to rule engine 218 with the updated variable value.For example, the user profile information is updated based on a newlyshared note from a mental health care professional indicating that auser is not friendly when talking to a chatbot. In some embodiments, theupdate is captured in an increased score related to the preferencecategory. The increased score triggers workflow manager 233 to checkwhether any workflow is currently being executed in connection to anyrequest from the user. If any, workflow manager 233 is triggered tomodify the steps to be performed by chatbots to steps awaitingprofessional processing. For another example, the user profileinformation is updated by a newly received lab report that a user isdiagnosed with hypertension and therefore might be in heightened need ofmental health related care. In some embodiments, such update is capturedin an increased score related to the mental health state category. Theincreased score triggers workflow manager 233 to check whether anyworkflow is currently being executed in connection to any request fromthe user. If any, workflow manager 233 is triggered to re-apply theruleset(s) respective to the workflow(s) to rule engine 218 to determinewhether the workflow(s) is to be modified based on the profile update.In this case, based on rule engine 218's re-evaluation of the ruleset, astep is added to the workflow (which is responsive to a non-mentalhealth care related request) so that a chatbot can proactively reach outto the user, either during the course of executing the workflow, or uponthe completion of the workflow, with regard to an assessment of mentalhealth wellness (using a survey pre-configured with the step).

In some embodiments, information obtained from the input sequence fromthe user during the execution of a workflow also triggers workflowmanager 233 to modify the workflow. For example, based on the previousand ongoing conversations, it is determined that the user is likely inthe middle of a crisis (e.g., based on the medical topic on whichservices have been requested, the frequency of the related requests, thenature of escalating issue related to the requests, detected user'smood, etc.). As such, the detection triggers workflow manager 233 tomodify the communication priority for the steps of the workflowassociated with the request.

In some embodiments, information observed, derived, learned fromcommunication and data transacted at platform 206 is captured as updatesto rulesets corresponding to user requests. In this case, changes in therulesets triggers workflow manager 233 to check whether any workflowdetermined based on the pre-update ruleset is currently being executedin connection to any request from the user. If any, workflow manager 233is triggered to re-apply the ruleset(s) to rule engine 218 using thelatest ruleset to determine whether the workflow needs to be modifiedbased on the ruleset changes. For example, a ruleset responding to aveterinary service request is updated to change the recommended fleacontrol medication for pets from brand A to brand B only in a particulargeo-area based on the communication and data transacted on platform 206related to pet flea control requests. As such, workflow manager 233 istriggered to re-apply the updated ruleset to all the pending requestsrelated to pet flea control in that geo-area. In some embodiments, theafore-described information is captured in the ruleset in the form ofinstructions for rule engines 218. For example, a change in theinstruction can cause rule engine 218 to ignore the order of evaluationbased on the orders of the scorings in various categories (e.g.,categories in the WPC survey), and instead to evaluate the rulesets in acategory with a lower user score sooner than the rulesets in a categorywith a higher user score.

Domain knowledge is used to generate rulesets and diagnosticquestionnaire. Domain knowledge includes information such as medicalknowledge, financial knowledge, mental health wellness knowledge, andthe like, and is stored in one or more databases (not shown). Domainknowledge can also be retrieved from third party databases of hospitals,healthcare facilities, and research facilities. Domain knowledge canalso be derived and/or self-compiled based on the communication and datatransacted at or available to platform 206. In other words, domainknowledge utilized by platform 206 can be expanded, updated anddeveloped based on the information gleaned, accumulated, and/orconducted by machine learning techniques through servicing a variety ofusers in a variety of service.

In some embodiments, steps of workflows are also generated based ondomain knowledge, such as clinical protocols, guidelines and bestpractices (e.g., established standards), in relation to conditions usersmay be suffering from or issues (e.g., financial issues, diet issues,mental health issues, etc.) on which they are seeking service/help atplatform 206. In some embodiments, based on the knowledge/understandingof user profile information (e.g., user preferences) gathered through awhole person care (WPC) survey, previous conversation on the samechannel and/or interactions on other channels, the workflow and itssteps (e.g., modes of communication, and types of communicationpreviously associated with the user) can be dynamically provisionedand/or modified during the delivery of the requested service.

For example, medical guidelines, protocols, best practice knowledge canbe used as medical domain knowledge, thereby forming the basis togenerate rulesets and corresponding diagnostic questionnaires that areinvoked in response to a user's request for clinical services. In someembodiments, transforming and/or mapping the guidelines, protocols andbest practice knowledge into rulesets and diagnostic questionnaires canbe performed by human operators (e.g., Jiseki engineers). In someembodiments, such transforming and/or mapping can be performedautomatically and/or reviewed by human operators, using techniques suchas artificial intelligence, and the like. In some embodiments, domainknowledge based rulesets and diagnostic questionnaires can be obtainedfrom third party providers.

Taking an upper respiratory tract infection (URTI) condition forexample, a diagnostic questionnaire is promulgated based on the domainknowledge obtained from various sources (e.g., private practice,clinics, guidelines, etc.) to include nine ordered questions. In someembodiments, the questions are designed and presented as either “yes” or“no” questions, multiple choice questions, or any combination thereof,to better accommodate online communication (e.g., web browser based orSMS based messaging, voice based messaging). In some embodiments, thediagnostic questionnaire is further enabled with tools/widgets/links forthe user to transmit information and data other than text or voice orvideo communication. For example, one question included in the URTIquestionnaire provides a set of instruction on how to take a picture atthe back of the user's throat, and a link for uploading the picturetaken.

In this example, the questions are presented to the user at a HIPAAcompliant platform in response to the user's service request (e.g.,#cold, #flu, #stuffy nose, #sorethroat, etc.). The questions arepresented in an order of from question 1 to question 9 so that, when theuser answers one of the questions in a certain way, the flow of thequestion either stops (done with gathering sufficient information), orbranches to all or some of the remaining questions. For example, onlywhen the user answers “yes” to question 1, “no” to question 2, and “yes”to question 5, question 7 is presented to the user. In general, thequestions at the beginning of the set tend to gather information aboutthe general aspects of the condition (e.g., running nose, cough, sorethroat, fever), and the later questions tend to gather information aboutaspects more specific and responsive to the information obtained throughthe previous questions (e.g., smoking history).

Still in the URTI example, the answers to the respective questions inthe diagnostic questionnaire are assigned with a score, and an overallscoring corresponding to the user's answers to one or more questions arecomputed. Based on comparing an overall scoring with a pre-configuredthreshold, a diagnosis can be formed in turn. For example, if the user'sanswer scores equally or greater than a threshold pre-configured inreference to question 4, question 6 and question 7, the user's conditionis diagnosed as flu. For another example, if the user's answer scoresequally or greater than a threshold pre-configured in reference toquestion 3, question 2 and question 5, the user's condition is diagnosedas sore throat. In the instant case, if the user answers “yes” to thequestion about a history of fever, one positive score is assigned to theanswer; if the user answers “no” to the question about the presence ofcoughs, one positive score is assigned to the answer; if the user isolder than 44, one negative score is assigned to the answer; and if theuser is younger than 15, one positive score is assigned to the answer.Then, an overall scoring is computed based the scores. If the overallscoring is between 0-2, it is concluded that the user should be treatedwith a low likelihood of group A strep condition; if the overall scoringis between 3-4, it is concluded that the user should be treated with anintermediate likelihood of group A strep condition; and if the overallscoring is greater than 4, it is concluded that the user should betreated with a high likelihood of group A strep condition. In someembodiments, the scores are weighted based on other factors such as age,geo-locations, other known medical conditions, financial status, and thelike. In this scenario, an overall scoring is computed based on both thescore and its respective weight.

Still yet in the URTI example, based on the domain knowledge thatgenerates the design of diagnostic questionnaire, as well as thepre-configuration of scores and their respective weights for associationwith various questions in the diagnostic questionnaire, a rulesetcorresponding to processing an URTI related service request is generatedand stored in ruleset store 234. The ruleset includes the one or morebranching conditions (scores and/or overall scoring with regard toanswers to particular questions, overall scores) contributing to thediagnosis of flu, sore throat, common cold, and rhinosinusitis, as wellas the levels of likelihood the user is infected with group A prep. Inthe instant case, various scores and/or scoring constitute variablevalues in a rule, various pre-configured thresholds constitute constantvalues in a rule. For example, a rule in the URTI ruleset can specifythat, if the scores/overall scoring are greater than a pre-configuredthreshold indicating a condition of flu, include a step of a chatbotsending a correspondent care plan to the user, and include a step of achatbot to follow up with the user after a pre-configured period oftime. Another rule in the URTI ruleset can specify that, if thescores/overall scoring are greater than a pre-configured thresholdindicating a condition of rhinosinusitis, include a step of setting upan appointment with a medical doctor, and include a step of a chatbot tofollow up with the user at a time after the appointment.

Ruleset store 234 includes a plurality of rulesets corresponding toresponding to service requests of the user. A ruleset can be indexedunder one or more corresponding service types (e.g., general type suchas #doc, specific type such as # rhinosinusitis). A ruleset contains oneor more rules, which are evaluated by rule engine 218. After making itsevaluation, rule engine 218 provides the result of its evaluation asoutput. The following is an example of a rule: if (score>8) and(score_y<6) then a; else if (score_x<4) then b; else c. A clause of arule (e.g., if (score_x>8) and (score_y<6) then a) includes one or moreconditions, one or more conjunction (and, or, and not, or not), aconnector (e.g., if, lese if, else), and provides one result. Acondition includes two arguments, which may be both variables, or onevariable and one constant. A condition includes one comparison evaluator(equal to, greater than, contains, etc.), and can further includes oneor more conjunctions (and, or, and not, or not). A variable listincludes one or more pairs of a variable name and variable value.Variable values are substituted into variables in arguments ofconditions.

In some embodiments, variable values are obtained from the scores and/orscoring of user's answers to various diagnostic questions. In someembodiments, variable values are further obtained from thescores/scoring stored in a profile of the user. Likewise, a rule canfurther specify at a finer granularity with regard to the conditions.Taking the rhinosinusitis related rule of URTI ruleset for example, thestep of a chatbot to follow up with the user at a time after theappointment can be further specified as, if the user profile indicates atendency of being good-natured upon a human conducting a follow up,include a step of a human agent to follow up with the user; otherwise,include a step of a chatbot to do so. In some embodiments, a rule of aruleset can also specify to evaluate one or more other rulesets upon thecondition been met.

In some embodiments, a ruleset addressing a service topic (e.g., cold)can include proactive rules that allows platform 206 to reach out to theuser to raise awareness/concerns about potential issues in the serviceareas other than the specific topic (e.g., another medical specialty,financial issues). For example, a rule in the URTI ruleset can specifythat, if the user's profile indicating a change of condition, e.g.,newly diagnosed hypertension, include a step of reaching out to the userwith mental health concerns. Such condition can be captured in a user'sreplies to a recent survey, or the user's lab results coming back toplatform 206 confirming a condition of hypertension.

Rule engine 218 performs evaluations of the rules of a ruleset in ruleset store 234 by first substituting values into variables, thenevaluating each condition of a clause, determining a result for eachclause, and finally determining a result for the entire rule based onthe results of the clauses. Rule engine 218 is configured to accessruleset 234 and response store 237, which store steps that can beassembled for each workflow. In some embodiments, rule engine 218 areimplemented using various techniques, such as Drools, JEOPS (JavaEmbedded Object Production System), OpenRules, etc.

In some embodiments, various surveys are conducted at platform 206 alsofor the purposes of generating rulesets and corresponding diagnosticquestionnaires, as well as gathering and/or updating the user profileinformation. For example, when subscribing to and/or logging for thefirst time onto platform 206, the user is presented with a whole personcare (WPC) survey. A WPC survey can be designed to gather userinformation on various categories, such as medical and wellness states,financial state, mental health state, household conditions, etc. In someembodiments, WPC scorings can be computed to assess triage factors foreach category. Similar to the scoring described above, a score and itsrespective weight is assigned to various answers in categories from theuser to a question. For example, for question that “when do you last seeyour primary care or nurse practitioner,” answers of “3 months ago,” “6months ago,” “a year ago” are assigned with a pre-configured score,respectively. Then, an overall scoring under each category is computedand stored in a profile associated with the user, representing acorresponding status factor. In some embodiments, periodical surveys,varying timing and topics, in one or more particular categories are sentto the user to gather new and/or updated information. In someembodiments, content and schedules of periodical surveys are customizedaccording the profile of the user, communication of other users, and thelike. The results returned by the user or the lack of returned resultsare similarly scored so as to update the overall scoring in eachcategory in the profile of the user. In some embodiments, the WPCscorings are updated based on communication and data available atplatform 206 in relation to servicing the user, and/or a group of users.All the scores and scorings stored in the user profile can be utilizedby rule engine 218 to evaluate rules that condition on such scores andscorings.

A “profile” as used herein, refers to a collection of information,observed or derived, about a user. A profile may include static datathat is not likely to change over time, and dynamic data that are likelyto change over time. For instance, a profile may include demographicaldata (retrieved from a demographic data store 239-1), subscription data,medical data (retrieved from an EMR store 239-2), financial data(retrieved from a financial data store 239-3), social media data(retrieved from a social media data store 239-n), user biometric data,user relationship data (family information, social network information,professional network information, relationship status, life events),user geo-presence data (residency location, nationality, travel routes),and the like of the user. A profile may also include preferences of theuser, either specified by the user, or observed or derived from theuser's communication and behaviors at platform 206. A profile may alsoinclude WPC scoring computed and adjusted over time for the user. Aprofile may also include notes and observations various chatbots, AIassistants, and professionals attached to the user via internal UI 214.A profile may also include user environment data such as informationabout the user's device, user' application data (e.g., calendarentries), etc. A profile may also include data from third party serviceprovided to platform 206 (e.g., lab results, imaging results). A profilemay also include a machine learning and/or AI model that classifiesintents, goals, moods of the user, determines the real meaning/semanticmeaning of the user on the user's input communication, or other users'communication. In some embodiments, the user profile information isbifurcated into two categories: HIPAA sensitive or non-HIPAA sensitivefor storage at separate data stores with appropriate security andprivacy guards.

In some embodiments, conversation management module 212 is configuredto, for example, classify, summarize, profile, rank, score, track theconversation, both in real time style via, for example, scoring andclassification sub-module 236 and real-time processing sub-module 235,and non-real time style, in order to both provision and modify themessage-based service for delivery, in real time or non-real timemanner. In some embodiments, processing, scoring, rankingfunctionalities sits behind workflow manager 233, determining whetherthe workflow needs adjustment.

Internal UI 214 includes a conversation dashboard 241, a workflowcontrol 242, a tags sub-module (e.g., a tagging sub-module) 243, a notesub-module (e.g., a note-taking sub-module) 244, and a workflow trackingsub-module 245. In some embodiments, conversation dashboard 241 isconfigured to communicate with conversation tracker 232 of conversationmanagement module 212, which communicates with router 224 of messageexecution module 210. In some embodiments, workflow control 242 isconfigured to communicate with ruleset store 234 of conversationmanagement module 212. In some embodiments, workflow control 242 allowsprofessionals manual interaction with the workflow (add a step, delete astep, modify a step, etc.); in some embodiments, it also allowsprofessional to change and store the ruleset corresponding to theworkflow for the user. More detail of internal UI 214 is described belowwith reference to FIGS. 4A-G.

In some embodiments, platform 206 enables Jisekiadministrative/engineering personnel to manage various aspectspertaining to professionals (e.g., agents) and administrators accessingplatform 206. For example, a Jiseki super administrator can organize theservices available at platform 206 into domains based on a variety ofcriteria. For instance, domains can be assigned based on geo-locationsso that services provided to users residing in one neighborhood, city,county, or the like can be aggregated in one domain. Alternatively,services provided to users communicating via a group of one or morechatbot servers can be aggregated in one domain. Domains can also bebased on the types of services, such as clinical, financial, mentalhealthcare, specialty of service domains (e.g., tax, URTI (upperrespiratory tract infection)), and the like. One domain can be furtherorganized into subordinate domains depending on the granularity deemednecessary or appropriate for delivering services at platform 206. Foranother example, a Jiseki administrator can create and manage variousflags for association with communication. For instance, a request forand communication related to a financial service can entail flagsrelated to credit card service (e.g., #card flag), financial advisorservice (e.g., #money flag), and the like. For an agent tasked withhandling medical related requests, such agent is further authorized toaccess only medical related flags, but not the communication flaggedunder legal or financial services. For yet another example, in additionto creating and managing accounts for individuals to log on, a Jisekiadministrator can authorize different access privileges to thecommunications available at platform 206 by assigning correspondentroles to the individual accounts. For instance, a Jiseki administratorcan create roles under which a collection of functionalities/actions canbe performed by the role holder. For example, an individual can beassigned a role of: admin, super admin, agent, concierge, financial,MCD, MCD concierge and talk, medical—Rx, mental health agent, and thelike. An individual assigned with an administrator role at a particulardomain is able to access all the communication (e.g., chats) and flagsin that domain. Such individual can also create and manage flagsassociated with the domain, as well as create and manage accounts ofother individuals servicing the domain (e.g., grant authorization toaccess HIPAA sensitive data stores). An individual assigned with anagent role at a particular domain can view the flags in the domain,request for new flags to be created, and access the communication (e.g.,chats) in the domain. An individual at a super admin role has all therights and privileges of administrators across all the domains. With theorganized domain structure, role assignment and flags assignment,platform 206 is able to scale effectively and efficiently despite alarge amount of user requests and interactions transacting thereat.

In some embodiments, platform 206 is a configured for creating,modifying, and managing workflows, using AI-based schemes (e.g.,intelligent processing/analysis, machine learning, etc.) such that toperform and/or assist service provisioning and modification for thepurposes of delivering message-based services. Artificial intelligence(AI) refers to computer systems which exhibit intelligent behaviorsincluding, for example and without limitations, the capacity to observe,learn, maintain a large store of data and knowledge; apply the abilitiesof reasoning, analyzing, inferring, summarizing, ranking etc. to dataand information, discern relationships between data and information;understand, perceive and make sense of data and information within thecontext, as well as communicate information (e.g., propose, suggest, orrecommend). In some examples, artificial intelligence systems create newknowledge, insights, best practices by finding previously unknownpatterns in data, and delivering new solutions by learning patterns indata.

In some embodiment, conversation management module 212 is configuredwith smart bots (not shown, and such as smart bots 109 a of FIG. 1A, aswell as bots executing on bot server 252 of FIG. 2B-1), and/or AIassistant (not shown, and such as AI assistance 109 b of FIG. 1A, aswell as third party service 263-a of FIG. 2B-2) which can assess and/orbe trained by data such as data of the user in a profile store 238,content of the conversation from both the user and platform 206 and/orservice professional (e.g., data in response store 237, data inconversation history 261 of FIG. 2B-1, JISEKI HIPAA data store 255 ofFIG. 2B-1), data obtained from, derived from, or pertaining to theafore-described features to classify, summarize, profile, rank, score,track of conversations, in relation to a single channel conversation(e.g., related to one type of service) of a same user, multiple channelsconversation (related to multiple types of services) of the same user,and/or any channel conversation of different users. In some embodiments,the training of bots/AI assistants is implemented at the level ofruleset, e.g., the rulesets in ruleset store 234 (e.g., analytics store267 of FIG. 2B-2) are trained with the afore-described information anddata so as to dictate the corresponding workflows carried out bots/AIassistants/professionals. In some embodiments, user profile informationand/or classify and summarize sub-module 231 are created, updated,and/or managed by AI models trained with the afore-described informationand data.

Moreover, various AI-based methods or machine learning methods can begenerated, managed, and maintained at platform 206. Artificialintelligence techniques can typically apply advanced mathematicalalgorithms, e.g., decision trees, neural networks, regression analysis,principal component analysis (PCA) for feature and pattern extraction,cluster analysis, genetic algorithm, or reinforced learning—to a dataset. In particular, platform 206 can employ one or more of methodologiesfor learning from data and then drawing inferences from the work flowmodels constructed. For example, Hidden Markov Models (HMMs) and relatedprototypical dependency models can be employed. General probabilisticgraphical models, such as Dempster-Shafer networks and Bayesian networkslike those created by structure search using a Bayesian model score orapproximation can also be utilized. In addition, linear classifiers,such as support vector machines (SVMs), non-linear classifiers likemethods referred to as “neural network” methodologies, fuzzy logicmethodologies can also be employed.

Furthermore, it should be appreciated that the neural networks used byplatform 206 are artificial neural network implementations, whichinclude one or more neural networks such as a feedforward neuralnetwork, a radial basis function network, a convolutional neuralnetwork, a recurrent neural network, a cascading neural network, aspiking neural network, a neuro-fuzzy network, or any other type ofneural network implementation. For example, with each new pair of userinput and one or more responses recorded at response store 237, theneural network layers of classify and summarize sub-module 231 get moreprecise to identify the intent, goal, information meant to becommunicated by the user, thus facilitating platform 206 to select,provision, and/or modify workflows to better service the user.

In some embodiments, classify and summarize sub-module 231, as well asAI models for creating, refining, updating, and/or managing rulesets inruleset store 234 and/or profiles in profile store 238 can be explicitlytrained (e.g., via identified training data), implicitly trained (e.g.,via observing communications, preferences, historical information,receiving extrinsic information such as social media data, user devicedata, user calendar data, user biometric data, user environment data,etc.), as well as trained in a hybrid manner (e.g., both explicitly andimplicitly). For example, SVMs can be configured via a learning ortraining phase within a classifier constructor and feature selections.Thus, classify and summarize sub-module 231 can be used to automaticallylearn and perform a number of functions, comprising but not limited todetermining according to a predetermined and/or learned criteria,priority, user goals, user intent, and/or user situations, a set of oneor more services that can be utilized to accomplish the user goal, asubset of services, resources to establish a selected service, etc. Thecriteria can comprise, but is not limited to, user profiles, historicalpatterns and/or trends, service provider preferences, predicted trafficflows, event data, current time/date, contact list data, subscriptiondata, location data, and so on. AI models in connection to ruleset store234 may output any information for creating and/or modifying a rulesetin ruleset store 234, such as but not limited to content of the ruleset,attributes associated with the steps of a workflow based on the ruleset,timing associated with the steps of a workflow based on the ruleset,evaluation instruction for rule engine 218. AI models in connection toprofile store 238 may output any information for creating and/orupdating a profile in profile store 238, such as but not limited toscores respective to categories corresponding to the services availableat platform 206, and the like. In some embodiments, a model controller(not shown) determines model parameters when creating a model using aneural network. For example, the model controller specifies how manyneurons to be used in the model, how many layers to be used in themodel, one or more of an amount of backpropagation, a dimension, and alearning rate with respect to the model. In implementation, thoseparameters can be configured as part of a TensorFlow configuration.

In some embodiments, the afore-described training of classify andsummarize sub-module 231 and AI models can be conducted iteratively. Insome embodiments, those afore-described AI-based schemes are carried outin a real time, near real time, or off-line manner. For example, in someembodiments, classify and summarize sub-module 231 is configured toanalyze an input sequence from the user in real time fashion todetermine, for example and without limitations, a ranking score in termsof emergency, priority, summarization of user' intent, and the like.

In some embodiments, AI models and classify and summarize sub-module 231may be trained with data relating to one or more users' communicationwith platform 206. The training data may take as input any type and formof information related thereto, such as but not limited to, the profilesof the users, the profile of the company/organization of the users,communication content in relation to previously requested same services,communication content in relation to previous services other than thecurrently requested one, date, temporal, location, and/or timinginformation, user device information, user social media information,other application information associated with the user (e.g., calendardata), user biometric information, user environment data, etc.

In some embodiments, a combination of AI-based schemes and serviceprofessionals is brought into conversations (e.g., message-based servicedelivery) without interfering with one and other in the course ofdelivering a service. In some embodiments, such a combination iscustomized for users based on the knowledge of the users that isaccessible or available at platform 206. For example, such knowledgeincludes the information (e.g., personal data such as gender, birthdate, occupation, medical history, financial history, background checks,preferred language for communication, other preferences, etc.) gatheredfrom the user at signing-up (e.g., signed up with the platform directlyor through a third party vendor, employer, etc.), through a whole personassessment survey other types of surveys, and the like. The knowledgecan also include information supplied from third parties such as creditscore of the user, credit/financial history of the user, litigationhistory of the user, and so forth. The knowledge can also includeinformation such as location, time, event, any contextual information,social media information, user device information, etc. The knowledgecan also include information from (e.g., gathered/retrieved/derivedfrom) previous/on-going/scheduled messaging sessions conducted by theuser with platform 206. For example, the language preferred by the userfor communication. The knowledge from previous/on-going/scheduledmessaging sessions can also include information from (e.g.,gathered/retrieved/derived from) previous/on-going/scheduled messagingsessions conducted by the user on a channel that provides the sameservice. The knowledge from previous/on-going/scheduled messagingsessions can also include information from (e.g.,gathered/retrieved/derived from) previous/on-going/scheduled messagingsessions conducted by the user on other channels that provide differentservices. The knowledge can also include information from (e.g.,gathered/retrieved/derived from) previous/on-going/scheduled messagingsessions conducted by other users, on the same channel providing thesame service, and/or on other channels providing different services. Insome embodiments, the workflow is modified based on the knowledge from(e.g., gathered/retrieved/derived from) on-going/scheduled messagingsessions conducted by the user and/or other users, on the same channelproviding the same service, and/or on other channels providing differentservices.

For example, a ruleset corresponding to a mental health service can betrained on data from (e.g., gathered/retrieved/derived from) thecommunication conducted by the same user at the channel providingfinancial service. In other words, platform 206 is configured tocross-train the ruleset with all or a portion of the data that is on(e.g., gathered/retrieved/derived on) platform 206, regardless of thetypes of services. Because of the integrated nature of service providingplatform 206, better, more accurate, more timely and more comprehensivedata is available from across-service sectors to serve as training datafor rulesets the platform. In some embodiments, such training data canbe further conditioned using profile/status/context/events/statisticsand the like. As such, improved and enhanced AI capabilities contributeto provisioning and modifying service workflows in a more accurate,personal, timely and informed manner for the purpose of deliveringservices. As a result, the improved service provisioning andmodification also increases the efficiency and accuracy of theinteraction between the user and the professional service providers,reduce the network bandwidth requirement in the system, and reduce thepower/computing resource consumption in the system.

Details of third party services 216 is described below with reference toFIG. 2B-2.

Further, it should be appreciated that one or more of the illustrativecomponents/modules in FIG. 2A may include other components,sub-components, modules, sub-modules, and device commonly found in acommunication/computing system, which are not discussed above withreference to Jiseki system and not discussed herein for clarity of thedescription. Additionally, in some embodiments, one or more of theillustrative components/modules can form a portion of anothercomponent/module and/or one or more of the illustrativecomponents/modules can be independent of one another.

FIGS. 2B-1 and 2B-2 (collectively, FIG. 2B) illustrate another exampleblock diagram of a portion of the architecture of a system for AIassisted service provisioning and modification for deliveringmessage-based service, in accordance with an embodiment of the presentdisclosure. In this example, system 250 depicts a user 280, an agent(either a clinician or a non-clinician agent) 290 a, as well as aclinician agent 290 b (collectively agent 290) in communicationtherewith. User 280 sends inputs to and receives responses fromcommunication module 251. In some embodiments, communication module 251includes one or more communication portals such as, for example andwithout limitations, Facebook Messenger 251 a, Twilio portal 251 b,browser portal 251 n, and the like. This way, communications of variousmodes (e.g., SMS, messaging applications, voice messaging, videomessaging, etc.) are enabled and supported between user 280 and system250. Furthermore, agent 290 connects to system 250 via a managementdashboard 260 (e.g., browser), and communicates with user 280 viacommunication module 251 as well.

Both communication module 251 and management dashboard 260 areconfigured in communication with a bot server 252. In some embodiments,a conversation history database 261 is configured to record a copy ofthe non-HIPAA-sensitive content of all or some of the conversationsexchanged between user 280 and system 250. For example, conversationhistory database 261 can record communication between user 280 andchatbots executing on bot server 252, between use 280 and non-clinicianagent 290 a, between user 280 and AI assistant (e.g., Sara) executing onan AI conversation engine 262, and the like. Similarly, HIPAA-sensitivecommunication and data related to user 280 is stored separately in aJiseki HIPAA store 255, which communicates with an EMR store 256.

In some embodiments, bot server 252 is configured to receive fromcommunication module 251 inputs from user 280, and send responses backto user 280 via communication module 251. In some embodiments, botserver 252 is configured to communicate with management dashboard 260 soas to forward to management dashboard 260 a copy of communication itemsbetween user 280 and bot server 252 (e.g., various chatbots executing onbot server 252). In some embodiments, bot server 252 is configured tocommunicate with management dashboard 260 to allow agent 290 to performvarious tasks related to creating and managing chatbots, such ascreating a chatbot, customizing a chatbot, loading a chatbot, deployinga chatbot, and delete a chatbot, etc. Bot server 252 can deploy achatbot into a user-interactive platform, such as webpages, intranet,widgets, messaging applications, social media applications, and thelike. In some embodiments, chatbots are created and managed usingvarious toolkits, such as Microsoft Bot Framework, BotKit, FacebookMessenger Platform, API.ai, Telegram Bot, and the like. In someembodiments, bot server 252 is configured to retrieve from one or morechatbotinfo databases 253 to load and/or deploy one or more chatbots(e.g., template chatbots) to carry out the workflow associated with theservice requested by user 280. In some embodiments, a bot generator 254can be used to run one or more scripts (e.g., master Java scripts) tocreate chatbots from scratch, or customize chatbots loaded fromchatbotinfo database 253. More details are described with reference toFIG. 4F below. In some embodiments, bot server 252 is also configured tointeract with a plurality of third party services 263 in connection todelivering requested services (e.g., execute steps of a workflow) touser 280.

In some embodiments, bot server 252 is implemented on an Amazon ElasticCloud (EC2) framework. In some embodiments, bot server 252 is configuredto provide one or more of the following functionalities: a load balancer(e.g., using Nginx), a cache (e.g., using Memcached), a web server(e.g., Apache Tomcat server), a Node.JS engine, a Facebook Reactlibrary, and a database storing pairs of key-value (e.g., using Redis).

In some embodiments, agent 290 b need to access privacy sensitive datasuch as electronic medical records 256 (EMR) of user 280 in order toprovide the requested service thereto. Clinician agent 290 b is usuallypre-authorized for access to EMR systems, or is able to request foraccess or qualified access to EMR systems. As used herein, clinicianagent 290 b may comprise a treating physician or physicians; specialistssuch as surgeons, radiologists, cardiologists, and oncologists;emergency medical technicians; physicians' assistants; nursepractitioners; nurses; nurses' aides; pharmacists; dieticians;microbiologists; laboratory experts; laboratory technologists; geneticcounselors; researchers; veterinarians; students; and the like.

In some embodiments, healthcare facilities such as hospitals or clinicshave EMR systems that store EMRs of patients, including clinical trialparticipants. EMRs may comprise electronic clinical documents such asimages, clinical notes, orders, summaries, reports, analyses, or othertypes of electronic medical documentation relevant to a particularpatient's condition and/or treatment. Electronic clinical documentscontain various types of information relevant to the condition and/ortreatment of a particular patient and can include information relatingto, for example, patient identification information, images, cultureresults, physical examinations, vital signs, past medical histories,surgical histories, family histories, histories of present illnesses,current and past medications, allergies, symptoms, past orders,completed orders, pending orders, tasks, lab results, other testresults, patient encounters and/or visits, immunizations, physiciancomments, nurse comments, other caretaker comments, and a host of otherrelevant clinical information.

In some embodiments, system 250 includes a HIPAA data store 255, whichis configured to interface chatbotinfo 253, EMR 256, payment form andHIPAA compliant survey module 257, and rule engine 259 in communicationwith analytics stores 267.

In some embodiments, HIPAA data store 255 is configured to store all ora portion of HIPAA-sensitive communication and data that has beentransacted via system 250. For example, HIPAA data store 255 can be usedto hold protected health information (PHI) such as information obtainedvia payment forms in connection of user 280 paying for health careservices or prescribed medication at system 250, and/or data obtainedvia HIPAA compliant surveys collected from user 280 by payment form andHIPAA compliant survey module 257 of system 250. In some embodiments,HIPAA data store 255 is configured to update, modify, or synchronize itsdata with the data of EMR 256. HIPAA data store 255 as well as thecomponents it interfaces with (e.g., components that make use of thedata of HIPAA data store 255, for example, rule engine 259, analyticsstore 267, WPC (whole person care) survey and analytics module 258.HIPAA data store 255 and EMR 256 are configured with security safeguardmeasures including, for example and without limitations, administrativesafeguards, physical safeguards, and technical safeguards. In someembodiments, technical safeguards are configured to implement accesscontrols, audit control, and integrity controls.

In some embodiments, bot server 252 is configured to interface with aplurality of services 263 in order to carry out the steps of a workflowassigned to user 280 in connection to delivering the requested service.Services 263 can include, for example and without limitations, an AIservice (e.g., an artificial entity under the name “Sara”) 263 a, avideo chat service 263 b, a natural language processing (NLP) service263 c, payment service 263 d, prescription service (Rx) 263 e, Jisekimember and services API 263 f, and a calendar service 263 n. In someembodiments, services 263 are provided by third party service providers.For example, AI service can be enabled by X2.ai and serviced by a X2conversation engine 262, which provides patients with access toaffordable and yet quality mental healthcare using psychologicalartificial intelligence; Wit.ai can be used to provide NLP services;Square can be used to provide payment services, GoGoMeds can be used toprovide prescription services; and Acuity can be used to providecalendar based appointment services, etc.

In some embodiments, services 263 can be implemented, assembled, orcustomized at system 250. For example, Jiseki member and service APIscan be implemented and/or customized using services provided from aplurality of third party providers 264 including, for example andwithout limitations, credit card service 264 a (e.g., PDS e-cash cardservice), dental service 264 b (e.g., Bento dental service), and paymentservice 264 n (e.g., Stripe credit card service). For another example,Stripe credit card service 264 n can be aggregated with calendar service263 n to streamline the steps required in a workflow that requires bothan appointment date selection and a pre-payment for the appointment.

Again, it should be appreciated that one or more of the illustrativecomponents/modules in FIG. 2B may include other components,sub-components, modules, sub-modules, and device commonly found in acommunication/computing system, which are not discussed above withreference to Jiseki system and not discussed herein for clarity of thedescription. Additionally, in some embodiments, one or more of theillustrative components/modules can form a portion of anothercomponent/module and/or one or more of the illustrativecomponents/modules can be independent of one another.

FIG. 3A-C illustrate flow charts of example processes for AI assistedservice provisioning and modification for delivering message-basedservices in accordance with an embodiment of the present disclosure.Processes 300, 330, and 340 can be implemented by, for example andwithout limitations, platform 106 of FIGS. 1A-C, cloud 108 of FIG. 1C,platform 206 of FIG. 2A, system 250 of FIG. 2B, and processor 802 ofFIG. 8. The following illustrate processes 300, 330, and 340 from theperspective of platform 206 of FIG. 2A.

Process 300 starts at 302, where an input sequence is received from auser in relation to a request for a service. In some embodiments,listener 221 of platform 206 receives the input sequence from the user.In some embodiments, the input sequence includes one or more inputitems. For example, an input item can include one or more tokens (e.g.,hashtags) recognizable at platform 206. At 302, the input sequence isprocessed to determine a service type. In some embodiments, router 224processes the input sequence to determine whether the one or more inputitems correspond to pre-defined tokens representing their respectiveservice type. In some embodiments, a voice based input sequence isconverted to text using speech-to-text techniques. In some embodiments,router 224 further utilizes a user profile information to determine aservice level accessible for the user, after matching the one or moreinput items to recognizable tokens.

At 306, the user request is associated with a workflow based at least inpart on the service type and a profile of the user, the workflow beingexecuted by at least one of: a chatbot, an AI assistant, or a serviceprofessional. The workflow includes a set of one or more steps, a stepof the set of one or more steps corresponds to a set of attributesincluding at least one of: a communication mode, a communication type,or a communication priority. In some embodiments, workflow manager 233retrieves from ruleset store 234 one or more rulesets according to theservice type, and applies the one or more rulesets, and/or data in theprofile of the user to rule engine 218 to generate a workflow.

At 308, the user is interacted with based at least in part on theworkflow. In some embodiments, step 308 further comprise steps 310through 316. At 310, a new input sequence is received at platform 206.At 312, the new input sequence is analyzed to determine a set of one ormore elements, an element of the set of one or more elements triggeringa change in the workflow. In some embodiments, classify and summarizesub-module 231 analyzes the new input sequence to determine whetherthere is any element that triggers a change in the workflow byclassifying, summarizing, ranking the new input sequence from the user.In some embodiments, an element corresponds an intent or goal of theuser. In some embodiments, an element corresponds to the values of thevariables utilized in rule conditions of the rules in the rulesets thatare used to determine the workflow.

At 314, in response to the determining that the set of one or moreelement is not empty, the workflow is modified based at least in part onthe set of one or more elements. In some embodiments, workflow manager233 modifies the workflow based on the determined set of one or moreelements. In some embodiments, independent from whether the set of oneor more elements is empty, workflow manager 233 modifies the workflowbased on the profile of the user and/or updates to the profile of theuser, using the profile data as one or more rule variable values forevaluating the rulesets to generate the workflow. In some embodiments,the updates to the profile trigger changes in the evaluation, and/orselection of the rulesets that are used to determine the workflow. Insome embodiments, workflow manager 233 modifies the workflow based onprevious communication with the user in relation to services of theservice type, and/or services of types other than the service type. Insome embodiments, workflow manager 233 modifies the workflow based oncommunication with other users on platform 206, in relation to servicesof the service type, and/or services of types other than the servicetype. At 316, the request is responded to based on the workflow.

FIG. 3B illustrates a flow chart of an example process for AI assistedservice provisioning and modification for delivering message-basedservices in accordance with an embodiment of the present disclosure.Process 330 starts at 332, where communication in relation tointeractions with a plurality of users is observed. In some embodiments,platform 206 records the communication (including data related to thecommunication) with all the user in response store 237. In someembodiments, platform 206 records pairs of user input and one or moreresponses in response store 237. In some embodiments, the communicationincludes surveys proactively transmitted to the user and relies back toplatform 206. At 334, the observed communication is analyzed to update aprofile for each respective user of the plurality of users, and/orrulesets corresponding to services requested by the plurality of users.In some embodiments, response store 237 and ruleset store 234 includesits respective intelligence with regard to analyzing the communication.In some embodiments, response store 237 and ruleset store 234 leverageon the functionalities of classification and summarize sub-module 231,and/or workflow manager 233 to analyze and update profiles and rulesets.In some embodiments, AI models are utilized to update the profile ofeach perspective user, and/or the rulesets based on which services areprovided. In some embodiment, the AI models are also trained and/orretrained in order to perform the updates. At 336, workflows associatedwith each respective user is modified based at least in part on theupdated perspective user profile, and/or corresponding one or morerulesets. In some embodiments, workflow manager 233 reapplies the datain the profile of a user to the ruleset which is used to generated theworkflow. In some embodiments, workflow manager 233 reapplies the datain the profile of a user to the modified ruleset that is used togenerate the workflow.

FIG. 3C illustrates a flow chart of an example process for AI assistedservice provisioning and modification for delivering message-basedservices in accordance with an embodiment of the present disclosure.Process 340 starts at 342, where a plurality of rulesets are establishedin a database for generating workflows, each ruleset of the plurality ofrulesets identifying one or more workflow, each rule of a ruleset havinga rule variable and a rule action. In some embodiments, Jisekiengineering creates rulesets corresponding to respective service types,surveys, and the like using established protocols, best practice,guidelines, surveys, etc. and stores the ruleset in ruleset store 234.At 344, a ruleset is selected from the plurality of rulesets in responseto user input. In some embodiments, workflow manager 233 selects one ormore rulesets based the service type determined using the user input. Insome embodiments, workflow manager 233 selects one or more rulesetsbased on the analysis results of the user input, such as answers tosurveys and/or questionnaires. In some embodiments, workflow manager 233selects a default ruleset using the WPC scoring in addition to theafore-described selection. At 346, values are obtained for the rulevariables from user input, and/or a profile of the user. In someembodiments, workflow manager 233 retrieves the correspondent data inthe profile of the user as variable values. In some embodiments,workflow manager 233 obtains variable values by analyzing the userinput, such as answers to surveys and/or questionnaires. At 348, theruleset(s) and the obtained values are applied to a rule engine (e.g.,rule engine 218) to generate a workflow, wherein a step of the workflowis retrieved from a step store (e.g., response store 237).

FIG. 4A illustrates a portion of a graphical representation of anexample user interface of internal UI 214 of FIG. 2A in accordance withan embodiment of the present disclosure. In some embodiments, aprofessional (e.g., doctors, counselors, therapists, personal financialadvisors, etc.) and/or a platform administrator is provided with aninternal user interface 214 (e.g., professional GUI) at platform 206. Insome embodiments, workflows are started with a bot (e.g., workflow arestarted in a Chatbot window) for a user who has interacted with platform206 beforehand (e.g., received message-based services at platform 206,registered with platform 206, etc.), or initiated at the user's request,or by professionals after they've learned more about the user. As shownherein, in some embodiments, via a chatbot provisioning window 400, aplatform administrator/professional provisions chatbots to serviceconversation requests. For example, the professional can specify onwhich topic (e.g., health related questions, finance related questions)a chatbot is to be provisioned by interacting with a topic picker 402,how often (e.g., on a daily basis, weekly basis) the chatbot is to beprovisioned by interacting with a frequency picker 404, and how manyusers the chatbot is tasked for interaction (e.g., initiate Bot for 1user, or for 2 users) by interacting with a quantity picker 406.

FIG. 4B illustrates a portion of a graphical representation of anotherexample user interface of internal UI 214 of FIG. 2A in accordance withan embodiment of the present disclosure. In some embodiments, aprofessional and/or a platform administrator is provided with aninternal user interface 214 (e.g., professional GUI) at platform 206. Asa portion of the display of a console dashboard 420 illustrates herein,a professional (e.g., an agent) can access an overview summary of allthe ongoing, completed and pending requests/conversations at platform206. In some embodiments, dashboard 420 displays the conservations withthe following categories of information: flags (412), organization(413), birth date (414), birth year (415), timestamp (416), channel(417), agent number (418), agent name (419), status of being assigned ornot (421), tags (422), and buttons to start chats (423). In thisexample, dashboard 420 displays a plurality of snippet rows 431-436 fora plurality of users accessing a platform 206. As illustrated herein,dashboard 420 shows relevant histories for all or a filtered subset ofthe users, including conversations with automation conducted during thecourse of delivering services to the users. In some embodiments, fordifferent professionals, dashboard 420 displays the portions of the usercommunication history that is pertinent to the particular professional,e.g., relevant as the context of a conversation stored with eachresponse indicates. For example, a financial advisor would only see thepart of a user's conversation history pertaining to financial issues. Insome embodiments, metadata associated with each conversation regardlessof the service type or context is displayed at dashboard 420 to alltypes of professionals. For example, a financial advisor cannot view thecontent of the chat between a user and a medical agent, but can view,for example, when and for how long the user communicated with themedical agent. If the medical agent has shared a note regarding the chatwith the user for the financial advisor (or all other agents servicingthe user), the financial advisor can view that note as well. In someembodiments, dashboard 420 allows searching for one or more particularusers, user communication history, etc. using, for example, anidentifier (such as a name or phone number of the one or more users). Insome embodiments, upon clicking on a “Chat” button displayed at each ofrows 431-436, the professional traverses to a conversation window (e.g.,window 450 of FIG. 4C), at which the professional may choose tocommunicate with the user manually and send a message immediately, orselect a communication mode (such as video chat), and/or specify acommunication type, such as sending a conversation to a bot or an AIassistant, with various buttons and menu choices (details are describedwith reference to FIG. 4C).

In some embodiments, dashboard 420 displays a status for pending servicerequests as “unassigned,” or the names of the professional, chatbot, orAI assistant the conservation has been assigned to. In some embodiments,dashboard 420 also includes a UI element (e.g., link) to allow reportingon, including, for example, information such as the number ofconversations, user administration, user statistics, professionalstatistics, bots/AI assistant statistics, etc. In some embodiments,dashboard 420 further includes one or more UI elements to allow definingrules as well.

FIG. 4C illustrates a portion of a graphical representation of anotherexample user interface of internal UI 214 of FIG. 2A in accordance withan embodiment of the present disclosure. In some embodiments, a serviceprofessional (e.g., doctors, counselors, therapists, personal financialadvisors, etc.) and/or a platform administrator is provided with aninternal user interface 214 (e.g., professional UI) at platform 206. Insome embodiments, a conversation window 450 is configured as part of theafore-described professional UI 214. In some embodiments, conversationwindow 450 includes an indicator 452 to reflect that the user hasalready been engaged with by a chatbot or other automation. Conversationwindow 450 also includes one or more buttons on a top panel 461. The setof one or more buttons include a “Notes” button 453, a “Mark Complete”button 454, a “HIPAA Invite” button 455, a “HIPAA Video Invite” button456, and a “Rx” button 457. Conversation window 450 also includes a “AddTag” button 458 in a mid-panel 462, a message displaying area in a lowerpanel 463, as well as a set of tabs in a bottom panel 464. The set oftabs includes a “Start Chat” tab 465, “Queue for Manual Get Back” tab466, and “Queue for Weekly Get Back” tab 467.

In some embodiments, a professional (e.g., an agent) can click the“Start Chat” tab 465 to begin a message-based session with a user, inresponse to receiving a user input. If the user has already beenserviced by a chatbot, the professional is then taking over theconversation. As illustrated herein, in response to the user sending aninput 471 (e.g., a SMS message of “#help”) having a respective timestamp472, the professional enters a response 473, which also has a respectivetimestamp 474. Further, the service professional can click on “Notes”button 453 to add notes about his or her interaction with the user;click on the “Mark Complete” button 454 to mark conversation as completeto indicate that this particular service request (e.g., issue-driveninteraction) has been fulfilled; click on the “HIPAA Invite” button 455to generate a special link for being texted to the user to invite him orher to a secure web chat; click on the “HIPAA Video Invite” 456 buttonto generate a special link for being texted to the user to invite him orher to a secure video web chat; click on the “Rx” button 457 (byauthorized professionals only) to initiate the process of writing aprescription; click on the “Add Tag” button 458 to tag the request forlater reference. For instance, after finding out more about what kind ofhelp the user is seeking, the professional can tag the request with, forexample, #plan, #sara, #talk, etc. In some embodiments, tags are similarto flags, which are described with more detail below.

Moreover, in response to receiving a user input (e.g., #help), and/or atany point during the conversation with the user, the professional canclick on “Queue for Manual Get Back” tab 466 to send/continue theservice request to special queues to follow-up manually; or click on“Queue for Weekly Get Back” tab 467 to send/continue the service requestto special queues to follow-up on a weekly basis, either by theprofessional or by an automation.

In some embodiments, users can flexibly interact with the platform viainput items (e.g., tokens, hashtags, keywords) to activate particularservices. The platform is configured to profile the users to provisioninitiation of automated conversations, manual conversations, and thecombination thereof based on the information available at the platform.In some embodiments, as described above, the platform is configured toconduct a whole person care survey (WPC survey) at the time a new usersubscribing to the platform for services, and stores the survey resultinformation in a profile store (e.g., profile store 238 of FIG. 2A). Thesurvey result information includes data such as user names, gender,birth date, address, phone number, email account, medical history,financial background, triage scoring, etc. The platform is alsoconfigured to collect data pertaining to the user, such as accesspatterns, content of the previously/on-going/scheduled conversationdelivering same and/or different types of services. Aprofessional/conversation GUI includes elements to accomplish this (asillustrated in FIGS. 4A-G) manually, or with suggestions from theplatform. Further, an internal data store (e.g., response store 237,ruleset store 234 of FIG. 2A) and workflow manager 233 (e.g., using ruleengine 218 of FIG. 2A) are connected through programmed rulesets toaccomplish the same automatically. In some embodiments, the rulesets areprogrammed by experts or learned via AI models.

FIG. 4D illustrates a portion of a graphical representation of yetanother example user interface of internal UI 214 of FIG. 2A inaccordance with an embodiment of the present disclosure. In someembodiments, UI 214 includes a dashboard window 480, which is configuredwith one or more tabs (e.g., HOME, CHATBOTS, CHATS, ANALYTICS, SETTINGS)on a top panel. The number of tabs on dashboard 480 can be configureddifferently depending on the roles of the agent at dashboard 480. Asshown herein, dashboard 480 is displayed with tabs “CHATS” and“SETTINGS”, with the tab “CHATS” 480-1 as the selected tab. On the lefthand panel, an status indicator is shown to confirm dashboard 480 asbeing connected to a chat server and therefore having access to themessages associated therewith. A notification indicator is also shown toreflect the setting of whether dashboard 480 is configured to playnotification sounds upon various alerts, or the like. An agent (e.g.,professional) interacting with dashboard 480 can toggle the notificationindicator to switch from the setting of not playing notification soundsto the setting of playing notification sounds, or vice versa. As shownherein, the agent chooses to let dashboard 480 play the notificationsound upon notifications. Further, the agent can click on either radiobutton of a radio button group 481-1 to select to group all thecommunication accessible at dashboard 480 (e.g., all the messagesaccessible at dashboard 480) either based on whether a chatbot hasalready been provisioned to engage the users, or based on the one ormore flags assigned to each user's request(s). More details about theusage of flags at dashboard 480 are described below with reference tothe right hand panel of dashboard 480.

Also at the left hand panel, dashboard 480 is configured to presentmultiple summaries with respect to the processing of the requestsreceived from the users. For example, a “Waiting for Agent” summary481-2 shows in its parenthesis how many users communicating via the chatservers associated with dashboard 480 are waiting to be assigned to anagent. As shown herein, a count of fifteen (15) user are waiting andtherefore, these requesting users have not been assigned to a respectiveagent. The count is updated in real time according to how many new userrequests are received at dashboard 480, as well as how many existentusers' requests have been assigned to agents. Additionally, a “ClaimedUsers” summary 481-3 shows in its parenthesis how many users have beenclaimed (e.g., assigned to) the agent interacting with dashboard 480. Insome embodiments, the agent, by claiming a user from a waiting listqueued up at dashboard 480, starts engaging with the user to handle theuser's service request. This way, the count of the total number of userswaiting for an agent decrease by one; and the count of the claimed usersassociated with the agent increase by one. On the other hand, once theagent is done fulfilling the user's request, the agent releases theclaim upon the user so that the count of the claimed user associatedwith the agent decrease by one. As shown herein, presently the count ofthe users claimed by the agent is five (5). Similarly, the count of theclaimed users is updated according to how many new users the agent hasclaimed to handle, as well as how many existent users the agent hasreleased.

Still at the left panel of dashboard 480, an “All User” panel 481-5 isconfigured to display a summary of a count of total users in itsparenthesis. As shown herein, a total of 24 entries of users aredisplayed in panel 481-5. In some embodiments, the count is computedbased on how many new users have started communication, how many usersare in the middle of being serviced, and how many users have completedcommunication at platform 206. Underneath the summary, a list of all theusers counted in the summary are displayed in, for example, a scrollablesub-panel. In some embodiments, a user is represented by a phone numberassociated therewith, the user's name, or the like. The agent can clickupon any user in the list to access the communication channel in themiddle panel. As shown herein, user “Chandra” 481-4 is selected andconsequently, the middle panel displays a first message “#doc” 482-1transmitted from user 481-4, as well as a series of messages 482-2generated by a chatbot (e.g., JisekiSaraForward) in response toreceiving first message 482-1. In some embodiments, when the agentstarts to enter a response in a message box 484 and/or clicks on an iconto cause the entered response to be transmitted to user 481-4, dashboard480 registers the action(s) as the agent claiming user 481-4 in a manneralternative and additionally to clicking on a “Claim User” button 483-2at the right hand panel. In some embodiments, dashboard 480 prompts theagent with a pop-up window (not shown) to confirm whether the agentindeed intends to terminate the chatbot (e.g., the active botJisekiSaraForward) which has been conversing with user 481-4. At thispoint, the agent can either choose to confirm (e.g., by clicking on a“Yes” button), or to cancel (e.g., by clicking on a “No” button) thetaking over action. In the first case, the agent claims selected user481-4 to start handling user 481-4's request manually. In the secondcase, the agent decides not to claim user 481-4 anyway, therebyreverting back the effect of the agent entering a response and sendingof the entered response. In some embodiments, dashboard 480 furtherprompts the agent to login under a role other than the current oneassociated with the logging-in account of the agent. For example,dashboard 480 can prompt the agent to log in as an administrator. Insome embodiments, upon being prompted, the agent can switch thelogging-in account to a super-agent or agent manager role so that theagent can further perform other actions, such as assigning user 481-4 toother agents under his/her management.

Now referring to the right hand panel of dashboard 480, at the top, an“Active Bot” section 483-1 is displayed to indicate that a chatbot ofthe type of JisekiSaraForward is presently assigned to engage user 481-4upon the receipt of an initial request (e.g., #doc). Upon clicking onthe “Claim User” button 483-2, the agent takes over interacting withuser 481-4. Similarly, in some embodiments, dashboard 480 prompts theagent with a pop-up window (not shown) to confirm whether the agentindeed intends to terminate the chatbot (e.g., the active botJisekiSaraForward) conversing with selected user 481-4. As theinteractions are substantially similar to the above-described withreference to the middle panel, the details are not repeated herein forthe purpose of simplicity.

After claiming user 481-4, in addition to allowing the agent to startcommunicating (e.g., text, video, HIPAA compliant communication, sendingattachment links) with selected user 481-4, dashboard 480 also allowsthe agent to perform a variety of actions in relation to handling theservice request from user 481-4. For example, the agent can record notesregarding user 481-4 by entering information into a note area 483-3. Theagent can also view a list of all the flags that have been associatedwith the service request of user 481-4 in a flag area 483-4. Further,the agent can assign new flag(s) to the instant service request byclicking on a “Add Flag” button 483-5. Upon the agent associating a newflag with the request, dashboard 480 updates the list entries in flagarea 483-4, as well as update the list/count of the users grouped bythat newly added flag so as to populate the left hand panel with thecurrent information.

Furthermore, dashboard 480 allows the agent to initiate and/or terminatecollaboration with other chatbots, AI assistants, and agents during thecourse of handling the instant service request from user 481-4. Forexample, the agent can click on a “Add Schedule” button 483-6 toschedule a chatbot to service user 481-4 by navigating to a CHATBOT tabof dashboard 480, the details of which is illustrated with reference toFIG. 4F below. The agent can also share the communication in response tothe instant service request by clicking on a “Share Chat” button 483-7.Upon the agent choosing to share the instant chat, dashboard 480 furtherprovides the agent with GUI elements (not shown) to select/specify whichother agent(s) to share the chat with, how the instant chat is to beshared, and the like. In some embodiments, the two-party communicationbetween user 481-4 and the agent becomes a group communication withadditional agent(s) with whom the chat has been shared.

FIG. 4E illustrates a portion of a graphical representation of anotherexample user interface of internal UI 214 of FIG. 2A in accordance withan embodiment of the present disclosure. In some embodiments, GUI 214includes a dashboard window 490, which is configured with a group oftabs of “HOME,” “CHATBOTS,” “CHATS,” “ANALYTICS,” and “SETTINGS.” Asshown herein, dashboard 490 is displayed with tab “CHATS” 484-2 as theactive tab. At the left hand panel, dashboard 490 displays a portion ofan “All Users” panel, which lists all the users grouped under the one ormore flags, e.g., #hablar, #help, #impuestos, #mcd, etc., associatedwith their respective requests. As shown herein, the agent has selectedthe group flagged with “#mcd” to expand in a dropdown list to show allthe users having requests flagged with #mcd (e.g., handled by one ormore professionals associated with the MCD organization). Similarly, therequesting users are represented by their names, phone numbers, or thelike. Here, user “Chandra” 484-1 is selected by the agent, and thecommunication occurred so far is displayed at the middle panel.

At the middle panel of dashboard 490, a portion of the communicatedmessages are displayed to the agent for viewing. For example, inresponse to the request (not shown) from user 484-1, a chatbot of thetype “JisekiSaraForward” has responded with a message 485-1; and user484-1 has further replied with a series of new input messages 485-2.

At the right hand panel of dashboard 490, a portion of the right handpanel is displayed to show some details of user 484-1 in an area of“User Details” 486. In some embodiments, user details 486 displaysinformation such as the user ID, name, email address registered, activebot assigned thereto, institution affiliation, member ID, and the like.At this point, user 484-1 is serviced by a chatbot of the type“JisekiSaraForward” and therefore the active bot assigned entryindicates “JisekiSaraForward.” Additionally, user 484-1 has also beenpreviously claimed by the agent (e.g., agent Nagaraj a, and the previouscommunication is not shown in this partial interface display), and theagent can click on “Release Claim” button in the “Claim by” area 486-2to stop handling the request of user 484-1. In other words, the agentcan remove user 484-1 from the list of users queuing to be serviced bythe agent.

FIG. 4F illustrates a portion of a graphical representation of anotherexample user interface of internal UI 214 of FIG. 2A in accordance withan embodiment of the present disclosure. In some embodiments, UI 214includes a dashboard window 490, which is configured with a group oftabs of “HOME,” “CHATBOTS,” “CHATS,” “ANALYTICS,” and “SETTINGS.” Asshown herein, dashboard 490 is displayed with tab “CHATBOTS” 480-2 asthe active tab. Under “CHATBOTS” tab 480-2, the agent can click on a“Create new bot” button 486-1 to create a chatbot without using anytemplate bots, or click on a “Load sample bot” button 486-2 to modify anexistent template bot into a new chatbot. A table of “Bot diagram”displays the information relating to all the created chatbots that canbe loaded and/or deployed. In some embodiments, the informationincludes, for example but not limited to, who creates the bot, a botname, a bot status, a bot type, and a time stamp associated with a bot(e.g., bot's deploy time). At column 487-1, an identification of (e.g.,names of an agent, an administrator) who creates the chatbot isdisplayed. For each bot displayed, at column 487-2, a “Load” button isdisplayed to allow the agent to load the respective bot; a “Deploy”button is displayed at column 487-3 to allow the agent to deploy therespective bot; and a recycle can icon is displayed at column 487-4 toallow the agent to delete the respective bot. For some bots, both theload button and the deploy button are active for selecting (e.g., theAirMartWelcome bot at row 1); while for some other bots (e.g.,tempbot212 at row2), only the load button is available while the deploybutton is unavailable (indicated with the hatching lines).

FIG. 4G illustrates a portion of a graphical representation of anotherexample user interface of internal UI 214 of FIG. 2A in accordance withan embodiment of the present disclosure. In some embodiments, UI 214includes a dashboard window 490, which is configured with a group oftabs of “HOME,” “CHATBOTS,” “CHATS,” “ANALYTICS,” and “SETTINGS.” Asshown herein, dashboard 490 is displayed with tab “SETTINGS” 490-1 asthe active tab. Under “SETTINGS” tab 490-1, the agent can configure avariety of settings for dashboard 490 using the links in the left handpanel. For example, by clicking on “Agents” link 490-2, the agent canaccess an agent table to view, search for, add, or remove agents. Forexample, by clicking on “Add Agent” button 490-3, the agent can add tothe agent group a new member by specifying the information such as theagent name, and the role assigned to the agent.

FIG. 5 illustrate a block diagram of an example context guard used inconnection with AI assisted service provisioning and modification fordelivering message-based services, in accordance with an embodiment ofthe present disclosure. As shown herein, a user (not shown) at a userdevice 502 communicates with a platform 506 via a network 504 in orderto request, and/or receive services provided at platform 506. In someembodiments, client 502 can be implemented by, for example and withoutlimitations, client 102 of FIGS. 1A-C; and network 504 can beimplemented by, for example and without limitations, network 104 ofFIGS. 1A-C. Platform 506 can be a part of one or more servers 106. Insome embodiments, tasks performed by platform 506 can be performed by aplurality of servers 106, e.g., being allocated amongst the plurality ofservers by an application, service, process, daemon, routine, executablelogic, or other task allocation techniques. In some embodiments,platform 506 can be implemented at, for example and without limitations,servers 106 of FIGS. 1A-B, and/or cloud 108 of FIG. 1C.

In some embodiments, multiple professionals communicate with (e.g.,text-messaging) a same user at the same communication modality on theuser's end (e.g., SMS, text-messaging application on the same device).Thus, it is important to ensure that the professionals don't clash ortalk over one another. Furthermore, it is also important to ensure thatthe professionals don't reveal confidential and/or privilegedcommunication to these who can access the communication from themodality of the user at platform 506, but not permitted with access to aparticular portion related to a particular service. For example, underthe legal rules, a medical doctor or any other professionals is notallowed to access the communication exchanged between the user and alegal professional at platform 506. On the contrary, for example, in thefields absent strict confidentiality rules, a professional (e.g., adietitian) and another (e.g., a personal trainer) may choose to alwaysshare communication with each other when it comes to consult the user atplatform 506 to deliver synergized results for the user. As acompromised way of sharing communication within respective servicefields, for example, a mental health care provider may leave a noteabout the user and specify what other professionals (e.g., doctors,nurses, social workers, financial advisor) can access the note about theuser based on confidential communication.

Platform 506 is configured with an internal signaling mechanism toachieve the above-described communication sharing. In some embodiments,the signaling is based on the service roles associated with theprofessional, chatbots, and AI assistants at platform 506. Essentially,when a bot, an AI assistant, or a professional starts to communicatewith a user (e.g., claims a user's request), platform 506 assigns theuser and that portion of the communicating a context, which is protectedby a corresponding context guard 510. This way, professionals atplatform 506 can only access portions of communication (e.g., chatsnippets) in a manner that is dictated by the permission associated withtheir roles. In other words, professionals only temporarily own thecontrol/access of the portion of the communication until the portionsare released (e.g., the chat is completed, or inactive after a period oftime). As shown herein, when the user engages a particular service area508 and thereby communicating with the associated professionals, bots,or AI assistants, context guard 510 is activated and attached to servicearea 508 such as to prevent professionals in other service areas fromentering the same conversation haphazardly. If, during the meanwhile ofthe user engaging platform 506 in service area 508, professionals in theother service areas 514 a, 514 b, . . . , 514 n will queue theircommunication into a conversation queue 512, or simply wait untilthey're no longer locked out from communicating with the user by contextguard 510. For example, during a financial advisor consultationcommunication, a medical follow-up chatbot is not allowed to interjectany communication in the ongoing consultation session, or view thecontent of the session. In some embodiments, some or all the metadata(e.g., timestamps, topics) pertaining to the conversation related toservice area 508 is allowed to be accessible or visible to otherprofessionals, chatbots, and AI assistants associated with the otherservice areas 514 a-n. In some other embodiments, a serviceprofessional, chatbot, or AI assistant associated with service area 508may choose to share a note related to the conversation in service area508 to the other professionals, chatbots, AI assistants associated withthe other service areas 514 a-n. In some embodiments, conversationtracking sets the context guard so that additional conversation isproperly assigned to that context and other contexts (includingworkflows operating within them) can't talk to the user until thetracking determines that the conversation is done.

In some embodiments, the ability to manage context guard allows platform506 to not only maintain separate conversation strands, but also toensure that information is properly shared amongst professionals,conversations are timely provisioned without interference with eachother. For example, a user may text a nurse, then consult with a doctor,and talk to the nurse again. All of these are within a medical contextallowing internal sharing. But if the user then messages a request for afinancial advisor, the conversation context has shifted, and thefinancial advisor, in addition to only being able to see parts of theconversation history related to that context, will trigger a separateworkflow to, for example, engage the user in a bot conversation aboutpersonal finances, or pass the user to, for example a tax lawyer. Thisway, the sequence of events and interactions won't affect or interferewith a separate doctor-triggered set of medical follow-up messages. And,while both these are going on, the user may be chatting with anemotional support AI assistant (e.g., sara) on demand which could chooseto follow-up on the user's emotional well-being at periodic intervalsshould the patient not message it for a specified period of time.

FIG. 6A illustrates example conversations between a user and an examplesystem for AI assisted service provisioning and modification inconnection with delivering message-based services, in accordance with anembodiment of the present disclosure. As depicted herein, three exampleconversations 601, 602 and 603 are illustrated at the user'sperspective. The first two conversations are engaged with professionals(e.g., a doctor and a financial advisor), and the third one conversationis, contingent on payment, a conversation with an chatbot or an AIassistant.

In conversation 601, a user at a messaging device 600 is requesting foradvice from a doctor via the system. In some embodiments, the system canbe implemented at, for example and without limitations, platform 106 ofFIGS. 1A-B, cloud 108 of FIG. 1C, platform 206 of FIG. 2A, and platform250 of FIG. 2B. To start the request, the user sends a text message of“#doc” to a short code of “543210” associated with the system.Subsequently, the user gets back a response of “That's it! Our clinicianstaff typically responds within 24-48 hours” from the system.

In conversation 602, the user at messaging device 600 is requesting foradvice from a doctor via the system. To start the request, the usersends a text message of “#doc” to a short code of “543210” associatedwith the system. Subsequently, the user gets back a response of “That'sit! Our clinician staff typically responds within 24-48 hours from thesystem.

In conversation 603, the user at messaging device 600 is requesting foron-demand emotional support via the system. To start the request, theuser sends a text message of “#Sara” to the same short code of “543210”associated with the system. Subsequently, the user gets back a responsefrom the system indicating that this type of service requires payment.Once the user makes the required payment, an example conversationexchanged between the user, Sara, and service professional areillustrated in Table 1.

In table 1 below, example dialog between a user that has engaged in aconversation initially conducted by a first bot (e.g., sara), thenhanded to a doctor, then another bot (e.g., survey bot), and finally thefirst bot (e.g., sara). It should be appreciated that a conversation canbe handed from a bot/AI assistant to other bots/AI assistants, anyprofessionals; from professionals to professionals; from professionalsto any bots/AI assistants.

TABLE 1 User Sara (emotional support bot/AI assistant) Notes on Handoffs#sara Hello, I'm Sara! Think of me as a source of support, to reach outwhenever you're feeling low, or when you just want to chat. While I amnot a therapist, I am trained by psychologists and take your privacyseriously! You can read more about the privacy policy here.http://bit.ly/2qTXihj. OK? It's ok I am eager to get to know you! What'syour name? Chandra It's great to meet you Chandra. Let's get started.I'd like you to take a moment to think about how your week has been andhow you're feeling. Tell me what comes to mind . . . I'm a little sadthis week. Very busy too. Thanks so much for sharing that. Have you feltthis way before? Sometimes Have you received support for this in thepast? Occasionally That's good to know, Chandra. It helps me to get asense of what you're going through. Would you like me to explain a bitmore about what you can expect from our time together? No, that's ok.Alright, since I am available anytime you need, it is best to start withone emotion or topic to focus on first. What comes to mind? Anxiety Doyou want to talk about feeling anxious, Chandra? Yes, a little That'sfine. I assume you prefer to stay on topic. Alright, since I amavailable anytime you need, it is best to start with one emotion ortopic to focus on first. What comes to mind? Anxiety I understand. I canoffer some strategies to cope with this. Should we go through onetogether? Yes Sometimes we convince ourselves of things that are notreally true. These types of things are not really true. These types ofthoughts are called cognitive distortions and usually reinforce negativethinking patterns. Bringing awareness to your thought patterns can helpreframe them in a more accurate and positive light. Allow me to providea few examples, ok? That's ok As we continue, think about how thesetypes of thoughts apply to you. It is important to understand your ownthought patterns A common distortion is called “all-or-nothing”thinking. An example of this is when you fail once and you convinceyourself that you will continue to fail at everything. Have youexperienced something similar? I've never but not sure For example, aperson with this mindset will think they are a complete failure if theyate a cookie while on a diet. Can you think of any instances where youhave had thoughts like this? Can't at the moment Okay good. Chandra!Re-attribution is a great way to fight the habit of assuming thateverything is your fault. Try switching your focus on solving theproblem instead of blaming yourself. Is that clear? Not clear Forexample, if a friend cancels dinner plans rather than blaming yourself,list other reasons why they might have cancelled. Perhaps they werefeeling financial stress, social anxiety, etc. Does this make sense?Sort of but It's okay, Chandra. That's why we're going through thesetogether. Being aware is the first step to combating these troublesomethoughts! It is also important to pay attention to your mental filter.Do you find yourself dwelling on the negatives and ignoring thepositives in your life? Now and then That's alright, Chandra. Focusingon the negative can be exhausting! Switching your focus can be veryuseful. Every time you focus on something negative in your life, try tolist a few positive things that you are grateful for! What is one thingyou are grateful for right now? Being busy Alright, let's continue I hada chat with a woman who repeatedly discredited her accomplishments. Sheinsisted that she was only offered a job because the company wasdesperate rather than because she had earned it. Do you ever think likethis? I do This type of thinking makes it hard to reward yourself. Itcan weigh you down, impacting your self-esteem and your relationships.This impact to relationships also occurs when we jump to conclusions. Doyou know what I mean? Not really Here is a specific example. Ben's crushhasn't talked to him in a few weeks so he assumes that she doesn't likehim and he withdraws from her. Later, he finds out she was preoccupiedwith family issues. Can you think of a time when you have jumped to aconclusion that wasn't right? I'm really I'm very sorry to hear that.Let me have “really depressed” depressed. I someone else talk to youabout that. and the context don't know about the user's what to do.anxiety triggers this message, and escalates it internally. Hi, this isDoc. Woodland. I'm a licensed If it is escalated counselor. Sara alertedme to talk to you. to a professional, Would that be alright? theprofessional informs the user who he is and ask the user's permission tocontinue. Sure Great. Can you tell me more about how you're feeling? I'mfeeling . . . (back and forth conversation continues) Can I ask you takea short survey about Professional is how you're feeling? It's medicallyasking the user to standard and It'll help me get you the go into a bot.help you need and deserve. Sure Great. I'll kick off the survey now.Professional picks the bot and assigns it to the user. Hi Chandra. Ihave a few questions to ask. It'll just take a few minutes. Ok Over thepast two weeks, have you found that you've had little interesting indoing things? Respond with 1 for Not at All, 2 for Several Days, 3 forMore than Half the Days, or 4 for Every Day 3 (bot conversationcontinues) That's it! Thanks for answering. I'll Bot passes back to apass the conversation back to Dr. Woodland professional. Hi Chandra.This is Dr. Woodland again. I have some thoughts based on yourresponses. Would you like to go through them now? Ok Professional isonce again talking to a user. Decides that the situation is not anemergency. Recommends an appointment. Here's the link to book aface-to-face appointment with a psychologist. <link> Please book as soonas you can. Ok In the meantime, would you like to talk to Sara again?Yes Great. I'll hand you off. Please text Professional #talk if you wantto talk to me or one reactivates Sara, of my colleagues again. We'rehere to the bot. help! Goodbye. Hi Chandra! This is Sara. Would you liketo talk about some techniques to manage stress?

FIG. 6B illustrates a graphic representation of an example userinterface showing another example conversations between a user and anexample platform for AI assisted service provisioning and modificationin connection with delivering message-based services. As shown herein,user interface 620 displays a message 622 (e.g., “#doc”) to a short code543-210. Subsequently, the platform responds with one or more messages624 with a timestamp 625. In some embodiments, in one of the one or moremessages 624, the platform provides the user with a link to travers tothe web page guide of the platform. Next, the user texts a message 626in a different language (e.g., #ayuda), and the platform responds withmessage 627 in the same different language as used in message 626.

FIGS. 7A-B illustrate a simplified flow diagram of an example workflowfor AI assisted service provisioning and modification for deliveringmessage based service, in accordance with an embodiment of the presentdisclosure. In this example, the workflow of conversations originatesfrom four domains: user 702, chatbot 704, Jiseki Administration 706, andJiseki control UI (e.g., internal UI 214 of FIG. 2A).

In some embodiments, the platform is configured with privacy safeguards(e.g., HIPAA compliance safeguards) so that the platform can engage auser in a conversation about, for example, personal finance topics,diet, or general well-being, without forcing the user into a more secureenvironment as one of the privacy concerns (e.g., HIPAA) is raised. Thisis determined based on information gathered as the workflow interactionrequires it, or a professional determines so. In some embodiments,mechanisms such as secure messaging, one-time access links are sent tousers so that to migrate/move them from normal messaging platforms tospecial web-hosted, browser-accessed secure environments, which conformto more stringent healthcare requirements but take users away from themessage platforms they're familiar with. In some embodiments, this isperformed on need-only basis. For example, manual and automatedconversations are allowed to be mixed flexibly, supporting moving theuser from normal conversation to HIPAA-compliant clinical conversations(including web calling) and back again.

In some embodiments, input tokens may be connected to complicated flowsleading to a mix of professional interventions and scripted services,such as illustrated in the flowchart of FIGS. 7A-B. As shown in FIGS.7A-B, a protocol-driven assessment for the medical condition (e.g.,rhinosinusitis) leads potentially to a prescription and the issuance ofa care plan to the user based on physician discretion. The platform(e.g., platform 106 of FIGS. 1A-B, cloud 108 of FIG. 1C etc.) allowscustomization of these workflows to include messaging across channels,involve partner companies, and encompass simple and complex exchanges ofinformation, service, and expertise. In FIGS. 7A-B, a Jiseki workflowwith a particular prescription-delivery partner is shown herein. Ingeneral, the workflow includes, for example, multiple actors andentities, automated follow-ups, secure modes of communication whenneeded, and payment processing.

In more details, at 721, a user 702 sends a text of “#rhnio” to a shortcode of “543210” associated with the platform, which in turn is receivedand responded to by a chatbot 704. In this example, chatbot 704 repliesto user 702 with a text message of “Hi this is the Jiseki Digital NurseAssistant. Before I let one of our MDs know about your prescriptionneeds for Acute Rhinosinusitis, can I ask you a few screening questions?(Y/N).” Upon receiving the message, user 702 may decide to rely with ananswer of “yes” or “no” at 724. When user 702 answers with a “yes,” at725, chatbot 704 sends to user 702 a message including a link for theHIPAA-compliant webpage for conducting the survey. In some embodiments,chatbot 704 notifies Jiseki Administration 706 to generate a secure linkwith the input such as the use's phone number at 723. On the other hand,when user 702 answers with a “no,” at 726, chatbot 704 sends anothermessage of “OK. We'll send your request to our clinician who'll respondto you directly within 48 hours” to user 702.

Once presented with the link sent from chatbot 704, user 702 clicks onthe secure link to go to the secure and HIPAA-compliant webpage, andstarts the screening via the webpage at 728. Once user 702 is engaged ina screening session using the link sent by chatbot 704, chatbot 704starts the screening sequence for user 702 at 729, if deciding that thisis an uncomplicated case.

In either case of chatbot 704 conducting the screening or a clinicianconducting the screening, at 727, a professional (e.g., a nurse) or anAI assistant at Jiseki control UI 708 sees user 702's data in aconversation queue, and subsequently sends out an email notification ofthe new inquiry to a physician. At 730, the physician (also at controlUI 708) engages user 702 with respect to the requested care by havingJiseki Administration 706 end a first message to user 702, indicatingthat “Jiseki here, our MD would like to ask you a few more questions.Click here <link> to start” at 734. Upon receiving the first message,user 702 clicks on the link embedded therein at 735 to see thephysician's message in the web environment and converse with thephysician in the web environment at 736. In some embodiments, JisekiAdministration 706 also sends user 702 a reminder in case where user 702does not respond to the first message sent at 734 after 48 hours.

Afterwards and at 731, the physician finishes the conversation with user702, and informs user 702 that the prescription will come or not needed,depending on the physician's opinion based on the conversation. In thescenarios where the physician does decide to prescribe medications foruser 702, at 739 and via control UI 708, the physician clicks on the“Rx” button (e.g., “Rx” button 457 of FIG. 4C) to fill information suchas the drug name, strength, amount, and instructions. The physician canalso select and attach a care plan (e.g., from a dropdown interfaceelement of control UI 708), and/or adds a message.

Next at 740, Jiseki Administration 706 generates a payment link (e.g., aStripe checkout link) for the prescription. At 741, chatbot 704 sends amessage to user 702 that the prescription has been approved, and themessage includes a link to complete a checkout. At 737, upon receivingthe message sent from chatbot 704 at 741, user 702 clicks on the linkand goes to the secure Stripe checkout page at 737, and in turn entersthe information required for the check out, such as name, address, phonenumber, email address, payment information etc., on the web based Stripecheckout form at 738.

Finally at 742, Jiseki Administration 706 triggers the creation of anSMS message to send the image of the prescription once the payment isprocessed. In cases where the physician attaches a care plan or anoptional message, Jiseki Administration 706 also sends to user 702 thecare plan, and/or the optional message. In some embodiments, JisekiAdministration 706 also sends a message including a link of options topurchase the prescription.

FIG. 8 is a functional diagram illustrating an embodiment of aprogrammed computer system for AI assisted service provisioning andmodification for delivering message-based services. As will be apparent,other computer system architectures and configurations can be used todisplay data. Computer system 800, which includes various subsystems asdescribed below, includes at least one microprocessor subsystem (alsoreferred to as a processor or a central processing unit (CPU)) 802. Forexample, processor 802 can be implemented by a single-chip processor orby multiple processors. In some embodiments, processor 802 is a generalpurpose digital processor that controls the operation of the computersystem 800. Using instructions retrieved from memory 810, the processor802 controls the reception and manipulation of input data, and theoutput and display of data on output devices (e.g., display 818). Insome embodiments, processor 802 includes and/or is used to provide thelaunch of a client application based on a message.

Processor 802 is coupled bi-directionally with memory 810, which caninclude a first primary storage area, typically a random access memory(RAM), and a second primary storage area, typically a read-only memory(ROM). As is well known in the art, primary storage can be used as ageneral storage area and as scratch-pad memory, and can also be used tostore input data and processed data. Primary storage can also storeprogramming instructions and data, in the form of data objects and textobjects, in addition to other data and instructions for processesoperating on processor 802. Also as is well known in the art, primarystorage typically includes basic operating instructions, program code,data, and objects used by the processor 802 to perform its functions(e.g., programmed instructions). For example, memory 810 can include anysuitable computer readable storage media, described below, depending onwhether, for example, data access needs to be bi-directional oruni-directional. For example, processor 802 can also directly and veryrapidly retrieve and store frequently needed data in a cache memory (notshown).

A removable mass storage device 812 provides additional data storagecapacity for the computer system 800 and is coupled eitherbi-directionally (read/write) or uni-directionally (read only) toprocessor 802. For example, storage 812 can also include computerreadable media such as magnetic tape, flash memory, PC-CARDS, portablemass storage devices, holographic storage devices, and other storagedevices. A fixed mass storage 820 can also, for example, provideadditional data storage capacity. The most common example of fixed massstorage 820 is a hard disk drive. Mass storage 812, 820 generally storeadditional programming instructions, data, and the like that typicallyare not in active use by the processor 802. It will be appreciated thatthe information retained within mass storages 812 and 820 can beincorporated, if needed, in standard fashion as part of memory 810(e.g., RAM) as virtual memory.

In addition to providing processor 802 access to storage subsystems, bus814 can also be used to provide access to other subsystems and devices.As shown, these can include a display 818, a network interface 816, akeyboard 804, and a pointing device 808, as well as an auxiliaryinput/output device interface, a sound card, speakers, and othersubsystems as needed. For example, the pointing device 808 can be amouse, stylus, track ball, or tablet, and is useful for interacting witha graphical user interface.

The network interface 816 allows processor 802 to be coupled to anothercomputer, computer network, or telecommunications network using anetwork connection as shown. For example, through the network interface816, the processor 802 can receive information (e.g., data objects orprogram instructions) from another network or output information toanother network in the course of performing method/process steps.Information, often represented as a sequence of instructions to beexecuted on a processor, can be received from and outputted to anothernetwork. An interface card or similar device and appropriate softwareimplemented by (e.g., executed/performed on) processor 802 can be usedto connect the computer system 800 to an external network and transferdata according to standard protocols. For example, various processembodiments disclosed herein can be executed on processor 802, or can beperformed across a network such as the Internet, intranet networks, orlocal area networks, in conjunction with a remote processor that sharesa portion of the processing. Additional mass storage devices (not shown)can also be connected to processor 702 through network interface 816.

An auxiliary I/O device interface (not shown) can be used in conjunctionwith computer system 800. The auxiliary I/O device interface can includegeneral and customized interfaces that allow the processor 802 to sendand, more typically, receive data from other devices such asmicrophones, touch-sensitive displays, transducer card readers, tapereaders, voice or handwriting recognizers, biometrics readers, cameras,portable mass storage devices, and other computers. Persons skilled inthe art may clearly understand that, for the sake of descriptiveconvenience and streamlining, one may refer to the processes in theaforesaid method embodiments that correspond to specific work processesof the systems, devices, and units described above. They will not bediscussed further here.

For a touch-sensitive display (also called “touch screen” or “touchdisplay screen”), the display an include a graphical user interface(GUI). The user may interact with the GUI display via various operationssuch as touching with a finger, touching with a hand, and/or gesture.Via the human-computer interactions, various functionalities can beachieved including: creating a web page, drawing, text processing,editing an electronic document, playing games, video conferencing,messaging, sending/receiving emails, making phone calls, playing video,playing audio, on-line browsing, and the like.

In one typical configuration, the computation equipment comprises one ormore processors (CPUs), input/output interfaces, network interfaces, andmemory.

Memory may include such forms as volatile storage devices incomputer-readable media, random access memory (RAM), and/or non-volatilememory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is an example of a computer-readable medium.

Computer-readable media, including permanent and non-permanent andremovable and non-removable media, may achieve information storage byany method or technology. Information can be computer-readable commands,data structures, program modules, or other data. Examples of computerstorage media include but are not limited to phase-change memory (PRAM),static random access memory (SRAM), dynamic random access memory (DRAM),other types of random access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, compact disk read-only memory(CD-ROM), digit multifunction disc (DVD) or other optical storage,magnetic cassettes, magnetic tape or magnetic disc storage, or othermagnetic storage equipment or any other non-transmission media that canbe used to store information that is accessible to computers. As definedin this document, computer-readable media does not include temporarycomputer-readable media, (transitory media), such as modulated datasignals and carrier waves.

A person skilled in the art should understand that embodiments of thepresent application can be provided as methods, systems, or computerprogram products. Therefore, the present application can take the formof an entirely hardware embodiment, an entirely software embodiment, oran embodiment that combines software and hardware aspects. In addition,the present application can take the form of computer program productsimplemented on one or more computer-operable storage media (includingbut not limited to magnetic disk storage devices, CD-ROMs, and opticalstorage devices) containing computer operable program codes.

Examples

Illustrative examples of the technologies disclosed herein are providedbelow. An embodiment of the technologies may include any one or more,and any combination of, the examples described herein below.

Example 1 includes a method for AI-assisted service provisioning andmodification for delivering message-based services, the methodcomprising receiving, by a computing system, an input sequence from auser in relation to a request for a service; classifying, by thecomputing system, the input sequence into a set of one or more elements,wherein an element of the set of one or more elements is associated withat least one of the following: a communication mode, a communicationtype, and a priority; associating, by the computing system, the userwith a workflow based at least in part on the set of one or moreelements; and interacting, by the computing system, with the user basedat least in part on the workflow to deliver the service.

Example 2 includes the subject matter of Example 1, and furtherincluding modifying, by the computing system, the workflow based oninteraction data in relation to the delivering of the service. Example 3includes the subject matter of any of Example 1-2, and wherein theworkflow is generated based at least in part on information pertainingto one or more best practices, and/or one or more guidelines related tothe service. Example 4 includes the subject matter of any of Example1-3, and wherein associating the user with the workflow is further basedat least in part on at least one of the following: data associated witha user profile of the user, data related to the interacting with theuser to deliver the service, and data related to interacting with theuser to deliver another service. Example 5 includes the subject matterof any of Example 1-4, wherein the modified workflow is storedcorresponding to the user. Example 6 includes the subject matter of anyof Example 1-5, and wherein modifying the workflow comprises modifyingthe workflow based at least in part on an interaction with the user onsession other than the current one.

Example 7 includes the subject matter of any of Example 1-6, and whereinmodifying the workflow comprises modifying the workflow based at leastin part on an interaction by the computing system with another user.Example 8 includes the subject matter of any of Example 1-7, and whereinthe input sequence from the user comprise a token indicating a field ofservice. Example 9 includes the subject matter of any of Example 1-8,and wherein the token is a hashtag. Example 10 includes the subjectmatter of any of Example 1-9, and wherein the workflow is provisionedwith a set of one or more workflow steps, a step of the set of one ormore workflow steps being performed by a bot, a professional personnel,a professional, or a combination thereof. Example 11 includes thesubject matter of any of Example 1-10, and wherein the session is amessaging based communication. Example 12 includes the subject matter ofany of Example 1-11, and furthering including associating, by thecomputing system, a context guard to a portion of the session betweenthe user and the computing system. Example 13 includes a computingsystem comprising a processor, and a memory having stored therein aplurality of instructions that when executed by the processor cause thecomputing system to perform the method of any of Examples 1-12. Example14 includes one or more machine-readable storage media comprising aplurality of instructions stored thereon that in response to beingexecuted cause a computing to perform the method of any of Examples1-12.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

Although the technologies described herein are primarily described withreference to the healthcare field, financial field, etc., it should beappreciated that, in other embodiments, the technologies can be employedin relation to other fields.

The embodiments of the present application are described with referenceto flowcharts and/or block diagrams based on methods, terminal equipment(systems), and computer program products of the embodiments of thepresent application. Please note that each flowchart and/or blockdiagram within the flowcharts and/or block diagrams and combinations offlowcharts and/or block diagrams within the flowcharts and/or blockdiagrams can be realized by computer commands. These computer programcommands can be provided to the processors of general-purpose computers,specialized computers, embedded processor devices, or other programmabledata processing terminals to produce a machine. The commands executed bythe processors of the computers or other programmable data processingterminal equipment consequently give rise to devices for implementingthe functions specified in one or more processes in the flowchartsand/or one or more blocks in the block diagrams.

The methods or algorithmic steps described in light of the embodimentsdisclosed herein can be implemented using hardware, processor-executedsoftware modules, or combinations of both. Software modules can beinstalled in random-access memory (RAM), memory, read-only memory (ROM),electrically programmable ROM, electrically erasable programmable ROM,registers, hard drives, removable disks, CD-ROM, or any other forms ofstorage media known in the technical field.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method for AI-assisted service provisioning andmodification for delivering message-based services, comprising:receiving an input sequence from a user in relation to a request for aservice, the input sequence including one or more input items;processing the input sequence to determine a service type; associating aworkflow with the request based on the service type and a profile of theuser, the workflow including a set of one or more steps, a step of theset of one or more steps corresponding to a set of attributes includingat least one of: a communication mode, a communication type, or acommunication priority, the workflow being performed by at least one of:a chatbot, an AI assistant, or a service professional; modifying theworkflow based on a new input sequence from the user using a workflowengine comprising at least one of: an AI model, a machine learningmodel, and a ruleset, wherein the workflow engine is configured toperform at least one action to workflows selected from a group ofactions composed of: starting, pausing, resuming, switching, updating,and stopping one or more of the workflows; and interacting with the userbased at least in part on the workflow to deliver the service.
 2. Themethod of claim 1, wherein the modifying of the workflow furthercomprises modifying the workflow based at least in part on communicationwith another user.
 3. The method of claim 1, wherein the profile of theuser is updated based at least in part on previous communication withthe user to deliver a service of the same service type, and/orcommunication with the user to deliver a service of a type other thanthe service type.
 4. The method of claim 1, wherein the workflow isgenerated by a rule engine evaluating a ruleset corresponding to theservice type.
 5. The method of claim 4, wherein a rule of the rulesetcorresponding to the service type is modified using an artificialintelligence model trained based at least in part on communicationrelated to delivering services of the service type.
 6. The method ofclaim 1, wherein the input sequence from the user comprises a tokenindicating a service type.
 7. The method of claim 6, wherein the tokenis a hashtag.
 8. The method of claim 1, wherein the modifying of theworkflow further comprises modifying the workflow based at least in parton the profile of the user.
 9. The method of claim 1, wherein one orboth of the input sequence and the new input sequence comprise at leastone of: textual data, symbolic data, audio data, video data, AR data,and VR data.
 10. The method of claim 1, wherein the interacting with theuser based at least in part on the workflow to deliver the servicecomprises messaging the user via at least one of: text communication,audio communication, video communication, AR communication, and VRcommunication.
 11. The method of claim 1, wherein the modifying theworkflow comprises modifying and managing the workflow based on aparameter associated with the service.
 12. The method of claim 1,wherein the parameter comprises a time constraint.
 13. The method ofclaim 1, wherein the workflow manager is configured to allow a humanoperator to perform at least one action selected from a group of actionsto workflows composed of: start, pause, resume, switch, update, and stopthe workflows.
 14. The method of claim 1, wherein the modifying theworkflow comprises cross-communications with a separate workflow viaoutputting to and taking input from the separate workflow.
 15. Themethod of claim 14, wherein the cross-communications with the separateworkflow comprise modifying at least one of: a prioritization factor, atiming factor, a number of message limit, message content for one orboth of the workflow and the separate workflow.
 16. The method of claim1, further comprising at least one of: storing the workflow, configuringan input mode, channel management, instruction management, rulemanagement, and capability management.
 17. The method of claim 1,wherein the workflow engine is trained based at least in part on one ormore of: historical communication with the user to deliver a service ofthe same service type, historical communication with the user to delivera service of a type other than the service type, historicalcommunication with another user to deliver a service of the same servicetype, historical communication with the another user to deliver aservice of a type other than the service type, and/or third-party datarelating to the same service type and/or a service of a type other thanthe service type, the third-party data including data supplied from atleast one of: a data service company, a partner, a public entity, agovernment entity, and a client.
 18. A method to establish a workflowengine, comprising: obtaining at least one of: historical dataassociated with historical interactions between a platform and aplurality of users, current data associated with ongoing interactionsbetween the platform and the plurality of users, third-party data,priorities associated with issues, time constraints, data gathered viainput from entities performing workflows; training the workflow engineto associate a workflow with an incoming request from a user; trainingthe workflow engine to detect cross-communication triggers in workflows;training the workflow engine to modify workflows based at least in parton at least one of: an AI model, a machine learning model, and aruleset; and executing the workflow engine to delivery services to theuser.
 19. The method of claim 18, wherein workflow engine is configuredto start, pause, stop, and modify the workflow.
 20. The method of claim18, wherein workflow engine is configured to update a profile associatedwith the user.
 21. A method for managing a plurality of workflowassociated with a user, comprising: monitoring a first workflowassociated with the user, the first workflow initiated in response to afirst new input sequence from the user; monitoring a second workflowassociated with the user, the second workflow initiated in response to asecond new input sequence from the user; detecting an interactionbetween the first workflow and the second workflow; and modifying atleast one of the first workflow and the second workflow based on thedetected interaction, wherein the modification comprises modifyingcontent delivered to the user by combining content generated inassociation with the first workflow and the content generated inassociation with the second workflow.
 22. The method of claim 21,wherein the modifying at least one of the first workflow and the secondworkflow based on the detected interaction comprises: determining atleast one of a priority, a timing, and a message limit associated withthe interaction; and modifying at least one of the first workflow andthe second workflow based on at least in part on at least one of the apriority, a timing, and a message limit.
 23. The method of claim 21,wherein the modifying at least one of the first workflow and the secondworkflow based on the detected interaction comprises modifying a firstnumber of messages generated in association with the first workflow anda second number of generated in association with the second workflowsuch that a sum of the first number and the second number does notexceed a preconfigured number.